{smcl}
{txt}{sf}{ul off}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\jungy\Dropbox\DISCRIMINATION PROJECT\Organizational Diversity\Statistics\Krause & Park.Authority Differentials.APPENDIX G RESULTS.08-07-2024.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 7 Aug 2024, 21:49:45
{txt}
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. ***** SUPPLEMENTARY APPENDIX STATISTICAL ANALYSES:  APPENDIX G: ADDITIONAL SENSITIVITY ANALYSES [OMITTING SUPERVISORY DESCRIPTIVE REPREENTATION CONTROL COVARIATE & REMOVING 'ABOVE PARITY' EXTREME SGPD VALUES] ******
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. *** ACCESS DATABASE FOR THE PROJECT: FEVS DATA FROM 2010-2019 AND 'MATCHED' OPM DATA ****
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. use "C:\Users\jungy\Dropbox\DISCRIMINATION PROJECT\Organizational Diversity\Statistics\2010-2019_DATA FINAL.08-07-2024.post-estimation.dta", replace 
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. *** ROBUSTNESS CHECK # 1: EVALUATE SENSITIVITY OF BASELINE MODEL SGPD ESTIMATES WHEN OMITTING SUPERVISORY DESCRIPTIVE/PASSIVE REPRESENTATION MEASURE AS A COVARIATE ***
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. *** 2.  CONDITIONAL-RESPONDENT MODELS EVALUATING THE RELATIONSHIP INVOLVING WITHIN-IDENTITY "OUT-GROUP" STATUS & BETWEEN-IDENTITY GROUP STATUS DIFFERENTIALS AS A MEANS TO FOSTER DIVERSITY AND INCLUSION IN THE U.S. CIVILIAN WORKFORCE ***   
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. *** MODEL G1.1: CONDITIONAL RESPONSES BY GENDER -- GENDER BETWEEN-IDENTITY GROUP STATUS DIFFERENTIAL MODEL: [WOMEN SUPERVISORS WITHIN AGENCY j IN YEAR t / MEN SUPERVISORS WITHIN AGENCY j IN YEAR t] / [WOMEN NON-SUPERVISORS WITHIN AGENCY j IN YEAR t / MEN NON-SUPERVISORS  WITHIN AGENCY j IN YEAR t]  -- CONTROLLING FOR GENDER SUPERVISORY EMPLOYEE IDENTITY GROUP DIFFERENTIAL ***
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. regress lndiversity2zeroadj  c.ln_ratio_fmsup_fmsub##i.gender    minority supervisor  topoffgender_2 lntotworkforce_count  ln_professionals_total_ratio   i.agencyid i.year, vce(cluster agencyid)

{txt}Linear regression                               Number of obs     = {res} 2,507,103
                                                {txt}{help j_robustsingular:F(16, 104) }       =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0404
                                                {txt}Root MSE          =    {res} .51453

{txt}{ralign 95:(Std. err. adjusted for {res:105} clusters in {res:agencyid})}
{hline 30}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 31}{c |}{col 43}    Robust
{col 1}          lndiversity2zeroadj{col 31}{c |} Coefficient{col 43}  std. err.{col 55}      t{col 63}   P>|t|{col 71}     [95% con{col 84}f. interval]
{hline 30}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}ln_ratio_fmsup_fmsub {c |}{col 31}{res}{space 2} .1528686{col 43}{space 2} .0420309{col 54}{space 1}    3.64{col 63}{space 3}0.000{col 71}{space 4} .0695198{col 84}{space 3} .2362174
{txt}{space 21}1.gender {c |}{col 31}{res}{space 2}-.0396463{col 43}{space 2} .0065106{col 54}{space 1}   -6.09{col 63}{space 3}0.000{col 71}{space 4}-.0525571{col 84}{space 3}-.0267354
{txt}{space 29} {c |}
gender#c.ln_ratio_fmsup_fmsub {c |}
{space 27}1  {c |}{col 31}{res}{space 2}-.0001761{col 43}{space 2} .0193477{col 54}{space 1}   -0.01{col 63}{space 3}0.993{col 71}{space 4}-.0385433{col 84}{space 3}  .038191
{txt}{space 29} {c |}
{space 21}minority {c |}{col 31}{res}{space 2}-.0944627{col 43}{space 2} .0037771{col 54}{space 1}  -25.01{col 63}{space 3}0.000{col 71}{space 4}-.1019529{col 84}{space 3}-.0869724
{txt}{space 19}supervisor {c |}{col 31}{res}{space 2} .1271052{col 43}{space 2} .0074362{col 54}{space 1}   17.09{col 63}{space 3}0.000{col 71}{space 4} .1123589{col 84}{space 3} .1418515
{txt}{space 15}topoffgender_2 {c |}{col 31}{res}{space 2}-.0027067{col 43}{space 2} .0047351{col 54}{space 1}   -0.57{col 63}{space 3}0.569{col 71}{space 4}-.0120966{col 84}{space 3} .0066831
{txt}{space 9}lntotworkforce_count {c |}{col 31}{res}{space 2}  .075019{col 43}{space 2} .0323468{col 54}{space 1}    2.32{col 63}{space 3}0.022{col 71}{space 4} .0108741{col 84}{space 3} .1391638
{txt}{space 1}ln_professionals_total_ratio {c |}{col 31}{res}{space 2} .0080447{col 43}{space 2} .0414898{col 54}{space 1}    0.19{col 63}{space 3}0.847{col 71}{space 4} -.074231{col 84}{space 3} .0903204
{txt}{space 29} {c |}
{space 21}agencyid {c |}
{space 27}2  {c |}{col 31}{res}{space 2} .3025752{col 43}{space 2} .1078801{col 54}{space 1}    2.80{col 63}{space 3}0.006{col 71}{space 4} .0886449{col 84}{space 3} .5165054
{txt}{space 27}3  {c |}{col 31}{res}{space 2} .0887289{col 43}{space 2} .0497385{col 54}{space 1}    1.78{col 63}{space 3}0.077{col 71}{space 4}-.0099044{col 84}{space 3} .1873621
{txt}{space 27}4  {c |}{col 31}{res}{space 2} .4274019{col 43}{space 2} .1205838{col 54}{space 1}    3.54{col 63}{space 3}0.001{col 71}{space 4} .1882796{col 84}{space 3} .6665241
{txt}{space 27}5  {c |}{col 31}{res}{space 2} .2578985{col 43}{space 2} .0912063{col 54}{space 1}    2.83{col 63}{space 3}0.006{col 71}{space 4} .0770329{col 84}{space 3} .4387641
{txt}{space 27}6  {c |}{col 31}{res}{space 2} .2463676{col 43}{space 2} .1105954{col 54}{space 1}    2.23{col 63}{space 3}0.028{col 71}{space 4} .0270527{col 84}{space 3} .4656824
{txt}{space 27}7  {c |}{col 31}{res}{space 2} .3456767{col 43}{space 2} .1535555{col 54}{space 1}    2.25{col 63}{space 3}0.026{col 71}{space 4} .0411704{col 84}{space 3}  .650183
{txt}{space 27}8  {c |}{col 31}{res}{space 2}  .294444{col 43}{space 2}  .126714{col 54}{space 1}    2.32{col 63}{space 3}0.022{col 71}{space 4} .0431655{col 84}{space 3} .5457225
{txt}{space 27}9  {c |}{col 31}{res}{space 2}-.0135302{col 43}{space 2} .0184197{col 54}{space 1}   -0.73{col 63}{space 3}0.464{col 71}{space 4}-.0500571{col 84}{space 3} .0229967
{txt}{space 26}10  {c |}{col 31}{res}{space 2} .2468997{col 43}{space 2} .1521969{col 54}{space 1}    1.62{col 63}{space 3}0.108{col 71}{space 4}-.0549123{col 84}{space 3} .5487118
{txt}{space 26}11  {c |}{col 31}{res}{space 2} .2058081{col 43}{space 2} .0751841{col 54}{space 1}    2.74{col 63}{space 3}0.007{col 71}{space 4} .0567152{col 84}{space 3} .3549011
{txt}{space 26}12  {c |}{col 31}{res}{space 2} .3676391{col 43}{space 2} .1527724{col 54}{space 1}    2.41{col 63}{space 3}0.018{col 71}{space 4} .0646858{col 84}{space 3} .6705924
{txt}{space 26}13  {c |}{col 31}{res}{space 2}  .354085{col 43}{space 2} .1428909{col 54}{space 1}    2.48{col 63}{space 3}0.015{col 71}{space 4}  .070727{col 84}{space 3} .6374429
{txt}{space 26}14  {c |}{col 31}{res}{space 2}  .159564{col 43}{space 2} .1060313{col 54}{space 1}    1.50{col 63}{space 3}0.135{col 71}{space 4}-.0507001{col 84}{space 3}  .369828
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{txt}{space 26}16  {c |}{col 31}{res}{space 2} .4606951{col 43}{space 2} .1782368{col 54}{space 1}    2.58{col 63}{space 3}0.011{col 71}{space 4} .1072448{col 84}{space 3} .8141454
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{txt}{space 26}27  {c |}{col 31}{res}{space 2} .3870003{col 43}{space 2} .1571744{col 54}{space 1}    2.46{col 63}{space 3}0.015{col 71}{space 4} .0753175{col 84}{space 3} .6986831
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{txt}{space 26}31  {c |}{col 31}{res}{space 2}-.0139908{col 43}{space 2}  .134121{col 54}{space 1}   -0.10{col 63}{space 3}0.917{col 71}{space 4}-.2799578{col 84}{space 3} .2519761
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{txt}{space 26}38  {c |}{col 31}{res}{space 2} .3842104{col 43}{space 2} .1588822{col 54}{space 1}    2.42{col 63}{space 3}0.017{col 71}{space 4} .0691411{col 84}{space 3} .6992797
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{txt}{space 26}41  {c |}{col 31}{res}{space 2} .2957234{col 43}{space 2} .1250411{col 54}{space 1}    2.37{col 63}{space 3}0.020{col 71}{space 4} .0477622{col 84}{space 3} .5436847
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{txt}{space 26}44  {c |}{col 31}{res}{space 2} .3498934{col 43}{space 2} .0957388{col 54}{space 1}    3.65{col 63}{space 3}0.000{col 71}{space 4} .1600397{col 84}{space 3}  .539747
{txt}{space 26}45  {c |}{col 31}{res}{space 2} .2941607{col 43}{space 2} .1043176{col 54}{space 1}    2.82{col 63}{space 3}0.006{col 71}{space 4}  .087295{col 84}{space 3} .5010263
{txt}{space 26}46  {c |}{col 31}{res}{space 2} .2724109{col 43}{space 2} .1633598{col 54}{space 1}    1.67{col 63}{space 3}0.098{col 71}{space 4}-.0515377{col 84}{space 3} .5963596
{txt}{space 26}47  {c |}{col 31}{res}{space 2} .2013754{col 43}{space 2} .0659751{col 54}{space 1}    3.05{col 63}{space 3}0.003{col 71}{space 4} .0705443{col 84}{space 3} .3322065
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{txt}{space 26}80  {c |}{col 31}{res}{space 2} .4289321{col 43}{space 2} .1602759{col 54}{space 1}    2.68{col 63}{space 3}0.009{col 71}{space 4}  .111099{col 84}{space 3} .7467653
{txt}{space 26}81  {c |}{col 31}{res}{space 2} .2143981{col 43}{space 2} .1703107{col 54}{space 1}    1.26{col 63}{space 3}0.211{col 71}{space 4}-.1233343{col 84}{space 3} .5521305
{txt}{space 26}82  {c |}{col 31}{res}{space 2} .3440912{col 43}{space 2} .1640216{col 54}{space 1}    2.10{col 63}{space 3}0.038{col 71}{space 4} .0188302{col 84}{space 3} .6693521
{txt}{space 26}83  {c |}{col 31}{res}{space 2} .4253423{col 43}{space 2} .1436768{col 54}{space 1}    2.96{col 63}{space 3}0.004{col 71}{space 4} .1404259{col 84}{space 3} .7102587
{txt}{space 26}84  {c |}{col 31}{res}{space 2} .3223875{col 43}{space 2} .1673536{col 54}{space 1}    1.93{col 63}{space 3}0.057{col 71}{space 4}-.0094809{col 84}{space 3} .6542559
{txt}{space 26}85  {c |}{col 31}{res}{space 2} .3986434{col 43}{space 2} .1122229{col 54}{space 1}    3.55{col 63}{space 3}0.001{col 71}{space 4} .1761013{col 84}{space 3} .6211856
{txt}{space 26}86  {c |}{col 31}{res}{space 2} .4541322{col 43}{space 2} .1768233{col 54}{space 1}    2.57{col 63}{space 3}0.012{col 71}{space 4}  .103485{col 84}{space 3} .8047793
{txt}{space 26}87  {c |}{col 31}{res}{space 2} .4564053{col 43}{space 2} .1643947{col 54}{space 1}    2.78{col 63}{space 3}0.007{col 71}{space 4} .1304045{col 84}{space 3} .7824061
{txt}{space 26}88  {c |}{col 31}{res}{space 2} .2481491{col 43}{space 2} .1146455{col 54}{space 1}    2.16{col 63}{space 3}0.033{col 71}{space 4} .0208027{col 84}{space 3} .4754954
{txt}{space 26}89  {c |}{col 31}{res}{space 2}   .29299{col 43}{space 2} .1409701{col 54}{space 1}    2.08{col 63}{space 3}0.040{col 71}{space 4}  .013441{col 84}{space 3} .5725389
{txt}{space 26}90  {c |}{col 31}{res}{space 2} .0698015{col 43}{space 2} .0418315{col 54}{space 1}    1.67{col 63}{space 3}0.098{col 71}{space 4} -.013152{col 84}{space 3}  .152755
{txt}{space 26}91  {c |}{col 31}{res}{space 2} .1884805{col 43}{space 2} .0906563{col 54}{space 1}    2.08{col 63}{space 3}0.040{col 71}{space 4} .0087057{col 84}{space 3} .3682553
{txt}{space 26}92  {c |}{col 31}{res}{space 2} .0814932{col 43}{space 2} .0456419{col 54}{space 1}    1.79{col 63}{space 3}0.077{col 71}{space 4}-.0090163{col 84}{space 3} .1720027
{txt}{space 26}93  {c |}{col 31}{res}{space 2} .4249284{col 43}{space 2} .1445918{col 54}{space 1}    2.94{col 63}{space 3}0.004{col 71}{space 4} .1381974{col 84}{space 3} .7116594
{txt}{space 26}94  {c |}{col 31}{res}{space 2} .4615151{col 43}{space 2} .1673593{col 54}{space 1}    2.76{col 63}{space 3}0.007{col 71}{space 4} .1296353{col 84}{space 3} .7933948
{txt}{space 26}95  {c |}{col 31}{res}{space 2} .2192225{col 43}{space 2} .1452131{col 54}{space 1}    1.51{col 63}{space 3}0.134{col 71}{space 4}-.0687405{col 84}{space 3} .5071855
{txt}{space 26}96  {c |}{col 31}{res}{space 2} .3353447{col 43}{space 2} .1447933{col 54}{space 1}    2.32{col 63}{space 3}0.023{col 71}{space 4} .0482141{col 84}{space 3} .6224753
{txt}{space 26}97  {c |}{col 31}{res}{space 2}   .38961{col 43}{space 2} .1259625{col 54}{space 1}    3.09{col 63}{space 3}0.003{col 71}{space 4} .1398217{col 84}{space 3} .6393983
{txt}{space 26}98  {c |}{col 31}{res}{space 2} .5538001{col 43}{space 2} .1787015{col 54}{space 1}    3.10{col 63}{space 3}0.002{col 71}{space 4} .1994282{col 84}{space 3} .9081719
{txt}{space 26}99  {c |}{col 31}{res}{space 2} .0815291{col 43}{space 2} .0246315{col 54}{space 1}    3.31{col 63}{space 3}0.001{col 71}{space 4} .0326839{col 84}{space 3} .1303742
{txt}{space 25}100  {c |}{col 31}{res}{space 2} .2517505{col 43}{space 2} .1473563{col 54}{space 1}    1.71{col 63}{space 3}0.091{col 71}{space 4}-.0404625{col 84}{space 3} .5439636
{txt}{space 25}101  {c |}{col 31}{res}{space 2} .4004648{col 43}{space 2} .1199324{col 54}{space 1}    3.34{col 63}{space 3}0.001{col 71}{space 4} .1626343{col 84}{space 3} .6382953
{txt}{space 25}102  {c |}{col 31}{res}{space 2} .2558221{col 43}{space 2}  .154484{col 54}{space 1}    1.66{col 63}{space 3}0.101{col 71}{space 4}-.0505255{col 84}{space 3} .5621698
{txt}{space 25}103  {c |}{col 31}{res}{space 2} .0683263{col 43}{space 2} .0773856{col 54}{space 1}    0.88{col 63}{space 3}0.379{col 71}{space 4}-.0851322{col 84}{space 3} .2217848
{txt}{space 25}104  {c |}{col 31}{res}{space 2}-.1076744{col 43}{space 2} .0397967{col 54}{space 1}   -2.71{col 63}{space 3}0.008{col 71}{space 4}-.1865928{col 84}{space 3} -.028756
{txt}{space 25}105  {c |}{col 31}{res}{space 2}  .330628{col 43}{space 2} .1433748{col 54}{space 1}    2.31{col 63}{space 3}0.023{col 71}{space 4} .0463103{col 84}{space 3} .6149456
{txt}{space 29} {c |}
{space 25}year {c |}
{space 24}2011  {c |}{col 31}{res}{space 2}-.0040633{col 43}{space 2} .0032861{col 54}{space 1}   -1.24{col 63}{space 3}0.219{col 71}{space 4}-.0105797{col 84}{space 3} .0024531
{txt}{space 24}2012  {c |}{col 31}{res}{space 2} .0002041{col 43}{space 2} .0044611{col 54}{space 1}    0.05{col 63}{space 3}0.964{col 71}{space 4}-.0086424{col 84}{space 3} .0090506
{txt}{space 24}2013  {c |}{col 31}{res}{space 2}-.0009147{col 43}{space 2}  .004687{col 54}{space 1}   -0.20{col 63}{space 3}0.846{col 71}{space 4}-.0102092{col 84}{space 3} .0083798
{txt}{space 24}2014  {c |}{col 31}{res}{space 2} -.006875{col 43}{space 2}  .006399{col 54}{space 1}   -1.07{col 63}{space 3}0.285{col 71}{space 4}-.0195646{col 84}{space 3} .0058145
{txt}{space 24}2015  {c |}{col 31}{res}{space 2}-.0144221{col 43}{space 2} .0079041{col 54}{space 1}   -1.82{col 63}{space 3}0.071{col 71}{space 4}-.0300962{col 84}{space 3}  .001252
{txt}{space 24}2016  {c |}{col 31}{res}{space 2}-.0176061{col 43}{space 2} .0067738{col 54}{space 1}   -2.60{col 63}{space 3}0.011{col 71}{space 4}-.0310387{col 84}{space 3}-.0041735
{txt}{space 24}2017  {c |}{col 31}{res}{space 2}-.0034765{col 43}{space 2} .0091073{col 54}{space 1}   -0.38{col 63}{space 3}0.703{col 71}{space 4}-.0215366{col 84}{space 3} .0145835
{txt}{space 24}2018  {c |}{col 31}{res}{space 2}-.0325368{col 43}{space 2} .0066852{col 54}{space 1}   -4.87{col 63}{space 3}0.000{col 71}{space 4}-.0457939{col 84}{space 3}-.0192798
{txt}{space 24}2019  {c |}{col 31}{res}{space 2} -.070852{col 43}{space 2}   .00806{col 54}{space 1}   -8.79{col 63}{space 3}0.000{col 71}{space 4}-.0868352{col 84}{space 3}-.0548687
{txt}{space 29} {c |}
{space 24}_cons {c |}{col 31}{res}{space 2} -.116202{col 43}{space 2} .4110626{col 54}{space 1}   -0.28{col 63}{space 3}0.778{col 71}{space 4}-.9313547{col 84}{space 3} .6989506
{txt}{hline 30}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16} 2,507,103{col 28} -1943049{col 39} -1891390{col 50}    17{col 58}  3782813{col 69}  3783030
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] IC note}}.{p_end}

{com}. *
. ** BETWEEN-IDENTITY "IN" GROUP VERSUS "OUT" GROUP STATUS DIFFERENTIAL BETWEEN GENDERED RESPONDENTS **
. 
. lincom c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .1528686{col 26}{space 2} .0420309{col 37}{space 1}    3.64{col 46}{space 3}0.000{col 54}{space 4} .0695198{col 67}{space 3} .2362174
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  1.gender#c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.gender#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.0001761{col 26}{space 2} .0193477{col 37}{space 1}   -0.01{col 46}{space 3}0.993{col 54}{space 4}-.0385433{col 67}{space 3}  .038191
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. *
. 
.    
. *** MODEL G2.1: CONDITIONAL RESPONSES BY RACE/ETHNICITY -- RACIAL/ETHNIC BETWEEN-IDENTITY GROUP STATUS DIFFERENTIAL MODEL: [MINORITY SUPERVISORS WITHIN AGENCY j IN YEAR t / NON-MINORITY SUPERVISORS WITHIN AGENCY j IN YEAR t] / [MINORITY NON-SUPERVISORS WITHIN AGENCY j IN YEAR t / NON-MINORITY NON-SUPERVISORS  WITHIN AGENCY j IN YEAR t] -- CONTROLLING FOR RACIAL/ETHNIC SUPERVISORY EMPLOYEE IDENTITY GROUP DIFFERENTIAL ***
. 
. regress  lndiversity2zeroadj  c.ln_ratio_mnmsup_mnmsub##i.minority     gender supervisor  topoffminority_2 lntotworkforce_count  ln_professionals_total_ratio   i.agencyid i.year, vce(cluster agencyid)

{txt}Linear regression                               Number of obs     = {res} 2,507,103
                                                {txt}{help j_robustsingular:F(16, 104) }       =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0403
                                                {txt}Root MSE          =    {res} .51456

{txt}{ralign 99:(Std. err. adjusted for {res:105} clusters in {res:agencyid})}
{hline 34}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 35}{c |}{col 47}    Robust
{col 1}              lndiversity2zeroadj{col 35}{c |} Coefficient{col 47}  std. err.{col 59}      t{col 67}   P>|t|{col 75}     [95% con{col 88}f. interval]
{hline 34}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}ln_ratio_mnmsup_mnmsub {c |}{col 35}{res}{space 2} .0376959{col 47}{space 2} .0360387{col 58}{space 1}    1.05{col 67}{space 3}0.298{col 75}{space 4}-.0337702{col 88}{space 3} .1091619
{txt}{space 23}1.minority {c |}{col 35}{res}{space 2}-.0780638{col 47}{space 2} .0058854{col 58}{space 1}  -13.26{col 67}{space 3}0.000{col 75}{space 4}-.0897348{col 88}{space 3}-.0663929
{txt}{space 33} {c |}
minority#c.ln_ratio_mnmsup_mnmsub {c |}
{space 31}1  {c |}{col 35}{res}{space 2} .0473003{col 47}{space 2} .0164833{col 58}{space 1}    2.87{col 67}{space 3}0.005{col 75}{space 4} .0146133{col 88}{space 3} .0799872
{txt}{space 33} {c |}
{space 27}gender {c |}{col 35}{res}{space 2}-.0395341{col 47}{space 2} .0041641{col 58}{space 1}   -9.49{col 67}{space 3}0.000{col 75}{space 4}-.0477917{col 88}{space 3}-.0312766
{txt}{space 23}supervisor {c |}{col 35}{res}{space 2} .1271766{col 47}{space 2} .0073844{col 58}{space 1}   17.22{col 67}{space 3}0.000{col 75}{space 4} .1125332{col 88}{space 3} .1418201
{txt}{space 17}topoffminority_2 {c |}{col 35}{res}{space 2} .0075238{col 47}{space 2} .0058786{col 58}{space 1}    1.28{col 67}{space 3}0.203{col 75}{space 4}-.0041337{col 88}{space 3} .0191814
{txt}{space 13}lntotworkforce_count {c |}{col 35}{res}{space 2}  .069522{col 47}{space 2}  .037308{col 58}{space 1}    1.86{col 67}{space 3}0.065{col 75}{space 4}-.0044612{col 88}{space 3} .1435052
{txt}{space 5}ln_professionals_total_ratio {c |}{col 35}{res}{space 2} .0189233{col 47}{space 2} .0468444{col 58}{space 1}    0.40{col 67}{space 3}0.687{col 75}{space 4}-.0739709{col 88}{space 3} .1118174
{txt}{space 33} {c |}
{space 25}agencyid {c |}
{space 31}2  {c |}{col 35}{res}{space 2} .2222603{col 47}{space 2} .1173938{col 58}{space 1}    1.89{col 67}{space 3}0.061{col 75}{space 4}-.0105359{col 88}{space 3} .4550566
{txt}{space 31}3  {c |}{col 35}{res}{space 2} .0232937{col 47}{space 2} .0550306{col 58}{space 1}    0.42{col 67}{space 3}0.673{col 75}{space 4}-.0858341{col 88}{space 3} .1324215
{txt}{space 31}4  {c |}{col 35}{res}{space 2} .2732202{col 47}{space 2} .1532204{col 58}{space 1}    1.78{col 67}{space 3}0.077{col 75}{space 4}-.0306217{col 88}{space 3}  .577062
{txt}{space 31}5  {c |}{col 35}{res}{space 2} .2076803{col 47}{space 2} .1087415{col 58}{space 1}    1.91{col 67}{space 3}0.059{col 75}{space 4}-.0079582{col 88}{space 3} .4233188
{txt}{space 31}6  {c |}{col 35}{res}{space 2}  .180734{col 47}{space 2} .1161916{col 58}{space 1}    1.56{col 67}{space 3}0.123{col 75}{space 4}-.0496784{col 88}{space 3} .4111463
{txt}{space 31}7  {c |}{col 35}{res}{space 2} .3247099{col 47}{space 2} .1797073{col 58}{space 1}    1.81{col 67}{space 3}0.074{col 75}{space 4}-.0316563{col 88}{space 3} .6810762
{txt}{space 31}8  {c |}{col 35}{res}{space 2} .2697052{col 47}{space 2} .1459933{col 58}{space 1}    1.85{col 67}{space 3}0.068{col 75}{space 4}-.0198049{col 88}{space 3} .5592154
{txt}{space 31}9  {c |}{col 35}{res}{space 2}-.0443881{col 47}{space 2} .0210445{col 58}{space 1}   -2.11{col 67}{space 3}0.037{col 75}{space 4}-.0861201{col 88}{space 3} -.002656
{txt}{space 30}10  {c |}{col 35}{res}{space 2} .2571549{col 47}{space 2} .1958139{col 58}{space 1}    1.31{col 67}{space 3}0.192{col 75}{space 4}-.1311513{col 88}{space 3} .6454611
{txt}{space 30}11  {c |}{col 35}{res}{space 2} .1983559{col 47}{space 2}  .086527{col 58}{space 1}    2.29{col 67}{space 3}0.024{col 75}{space 4} .0267696{col 88}{space 3} .3699421
{txt}{space 30}12  {c |}{col 35}{res}{space 2} .3316206{col 47}{space 2} .1962209{col 58}{space 1}    1.69{col 67}{space 3}0.094{col 75}{space 4}-.0574928{col 88}{space 3}  .720734
{txt}{space 30}13  {c |}{col 35}{res}{space 2} .3222659{col 47}{space 2} .1579603{col 58}{space 1}    2.04{col 67}{space 3}0.044{col 75}{space 4} .0090247{col 88}{space 3} .6355071
{txt}{space 30}14  {c |}{col 35}{res}{space 2} .1475316{col 47}{space 2} .1110479{col 58}{space 1}    1.33{col 67}{space 3}0.187{col 75}{space 4}-.0726805{col 88}{space 3} .3677437
{txt}{space 30}15  {c |}{col 35}{res}{space 2} .3142081{col 47}{space 2} .1213837{col 58}{space 1}    2.59{col 67}{space 3}0.011{col 75}{space 4} .0734998{col 88}{space 3} .5549165
{txt}{space 30}16  {c |}{col 35}{res}{space 2} .3912932{col 47}{space 2} .1939678{col 58}{space 1}    2.02{col 67}{space 3}0.046{col 75}{space 4} .0066479{col 88}{space 3} .7759386
{txt}{space 30}17  {c |}{col 35}{res}{space 2} .1133754{col 47}{space 2} .1549568{col 58}{space 1}    0.73{col 67}{space 3}0.466{col 75}{space 4}-.1939097{col 88}{space 3} .4206605
{txt}{space 30}18  {c |}{col 35}{res}{space 2} .3519459{col 47}{space 2} .1542636{col 58}{space 1}    2.28{col 67}{space 3}0.025{col 75}{space 4} .0460354{col 88}{space 3} .6578565
{txt}{space 30}19  {c |}{col 35}{res}{space 2} .2038253{col 47}{space 2} .1049199{col 58}{space 1}    1.94{col 67}{space 3}0.055{col 75}{space 4}-.0042348{col 88}{space 3} .4118854
{txt}{space 30}20  {c |}{col 35}{res}{space 2} .0880625{col 47}{space 2} .1112186{col 58}{space 1}    0.79{col 67}{space 3}0.430{col 75}{space 4}-.1324882{col 88}{space 3} .3086132
{txt}{space 30}21  {c |}{col 35}{res}{space 2} .2376871{col 47}{space 2} .0977008{col 58}{space 1}    2.43{col 67}{space 3}0.017{col 75}{space 4} .0439427{col 88}{space 3} .4314316
{txt}{space 30}22  {c |}{col 35}{res}{space 2} .2121988{col 47}{space 2} .1456542{col 58}{space 1}    1.46{col 67}{space 3}0.148{col 75}{space 4}-.0766389{col 88}{space 3} .5010365
{txt}{space 30}23  {c |}{col 35}{res}{space 2}-.0477614{col 47}{space 2} .0614261{col 58}{space 1}   -0.78{col 67}{space 3}0.439{col 75}{space 4}-.1695717{col 88}{space 3} .0740488
{txt}{space 30}24  {c |}{col 35}{res}{space 2}  .266002{col 47}{space 2} .1117835{col 58}{space 1}    2.38{col 67}{space 3}0.019{col 75}{space 4}  .044331{col 88}{space 3}  .487673
{txt}{space 30}25  {c |}{col 35}{res}{space 2} .2321075{col 47}{space 2} .1253844{col 58}{space 1}    1.85{col 67}{space 3}0.067{col 75}{space 4}-.0165344{col 88}{space 3} .4807494
{txt}{space 30}26  {c |}{col 35}{res}{space 2}  .079015{col 47}{space 2} .1138826{col 58}{space 1}    0.69{col 67}{space 3}0.489{col 75}{space 4}-.1468184{col 88}{space 3} .3048484
{txt}{space 30}27  {c |}{col 35}{res}{space 2}  .356406{col 47}{space 2} .1864655{col 58}{space 1}    1.91{col 67}{space 3}0.059{col 75}{space 4}-.0133621{col 88}{space 3} .7261741
{txt}{space 30}28  {c |}{col 35}{res}{space 2}-.0066467{col 47}{space 2} .0896139{col 58}{space 1}   -0.07{col 67}{space 3}0.941{col 75}{space 4}-.1843545{col 88}{space 3} .1710611
{txt}{space 30}29  {c |}{col 35}{res}{space 2} .1199772{col 47}{space 2} .1630935{col 58}{space 1}    0.74{col 67}{space 3}0.464{col 75}{space 4}-.2034432{col 88}{space 3} .4433976
{txt}{space 30}30  {c |}{col 35}{res}{space 2} .1793731{col 47}{space 2} .1540382{col 58}{space 1}    1.16{col 67}{space 3}0.247{col 75}{space 4}-.1260905{col 88}{space 3} .4848367
{txt}{space 30}31  {c |}{col 35}{res}{space 2} .0286951{col 47}{space 2} .1589861{col 58}{space 1}    0.18{col 67}{space 3}0.857{col 75}{space 4}-.2865804{col 88}{space 3} .3439706
{txt}{space 30}32  {c |}{col 35}{res}{space 2} .2424454{col 47}{space 2} .1252111{col 58}{space 1}    1.94{col 67}{space 3}0.056{col 75}{space 4}-.0058529{col 88}{space 3} .4907437
{txt}{space 30}33  {c |}{col 35}{res}{space 2} .1574121{col 47}{space 2} .0716892{col 58}{space 1}    2.20{col 67}{space 3}0.030{col 75}{space 4} .0152497{col 88}{space 3} .2995745
{txt}{space 30}34  {c |}{col 35}{res}{space 2} .0465237{col 47}{space 2}   .11262{col 58}{space 1}    0.41{col 67}{space 3}0.680{col 75}{space 4} -.176806{col 88}{space 3} .2698535
{txt}{space 30}35  {c |}{col 35}{res}{space 2} .1273461{col 47}{space 2} .1017938{col 58}{space 1}    1.25{col 67}{space 3}0.214{col 75}{space 4}-.0745149{col 88}{space 3} .3292071
{txt}{space 30}36  {c |}{col 35}{res}{space 2} .2176083{col 47}{space 2} .1302284{col 58}{space 1}    1.67{col 67}{space 3}0.098{col 75}{space 4}-.0406396{col 88}{space 3} .4758562
{txt}{space 30}37  {c |}{col 35}{res}{space 2}  .184029{col 47}{space 2} .1161487{col 58}{space 1}    1.58{col 67}{space 3}0.116{col 75}{space 4}-.0462982{col 88}{space 3} .4143562
{txt}{space 30}38  {c |}{col 35}{res}{space 2}  .355878{col 47}{space 2} .1766207{col 58}{space 1}    2.01{col 67}{space 3}0.046{col 75}{space 4} .0056325{col 88}{space 3} .7061235
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{txt}{space 29}102  {c |}{col 35}{res}{space 2}  .319034{col 47}{space 2} .1874472{col 58}{space 1}    1.70{col 67}{space 3}0.092{col 75}{space 4} -.052681{col 88}{space 3} .6907489
{txt}{space 29}103  {c |}{col 35}{res}{space 2} .1257802{col 47}{space 2} .0943634{col 58}{space 1}    1.33{col 67}{space 3}0.185{col 75}{space 4}-.0613461{col 88}{space 3} .3129064
{txt}{space 29}104  {c |}{col 35}{res}{space 2}-.1226546{col 47}{space 2} .0500611{col 58}{space 1}   -2.45{col 67}{space 3}0.016{col 75}{space 4}-.2219276{col 88}{space 3}-.0233816
{txt}{space 29}105  {c |}{col 35}{res}{space 2}  .288249{col 47}{space 2}  .170112{col 58}{space 1}    1.69{col 67}{space 3}0.093{col 75}{space 4}-.0490895{col 88}{space 3} .6255874
{txt}{space 33} {c |}
{space 29}year {c |}
{space 28}2011  {c |}{col 35}{res}{space 2}-.0017529{col 47}{space 2} .0039386{col 58}{space 1}   -0.45{col 67}{space 3}0.657{col 75}{space 4}-.0095632{col 88}{space 3} .0060574
{txt}{space 28}2012  {c |}{col 35}{res}{space 2} .0065821{col 47}{space 2} .0048785{col 58}{space 1}    1.35{col 67}{space 3}0.180{col 75}{space 4}-.0030921{col 88}{space 3} .0162563
{txt}{space 28}2013  {c |}{col 35}{res}{space 2} .0056973{col 47}{space 2}  .005746{col 58}{space 1}    0.99{col 67}{space 3}0.324{col 75}{space 4}-.0056973{col 88}{space 3} .0170918
{txt}{space 28}2014  {c |}{col 35}{res}{space 2} .0018719{col 47}{space 2}   .00627{col 58}{space 1}    0.30{col 67}{space 3}0.766{col 75}{space 4}-.0105617{col 88}{space 3} .0143056
{txt}{space 28}2015  {c |}{col 35}{res}{space 2}-.0046869{col 47}{space 2} .0071812{col 58}{space 1}   -0.65{col 67}{space 3}0.515{col 75}{space 4}-.0189274{col 88}{space 3} .0095536
{txt}{space 28}2016  {c |}{col 35}{res}{space 2}-.0061767{col 47}{space 2} .0063698{col 58}{space 1}   -0.97{col 67}{space 3}0.334{col 75}{space 4}-.0188084{col 88}{space 3} .0064549
{txt}{space 28}2017  {c |}{col 35}{res}{space 2} .0057176{col 47}{space 2} .0097788{col 58}{space 1}    0.58{col 67}{space 3}0.560{col 75}{space 4}-.0136741{col 88}{space 3} .0251093
{txt}{space 28}2018  {c |}{col 35}{res}{space 2} -.019585{col 47}{space 2} .0069283{col 58}{space 1}   -2.83{col 67}{space 3}0.006{col 75}{space 4} -.033324{col 88}{space 3}-.0058459
{txt}{space 28}2019  {c |}{col 35}{res}{space 2}-.0576999{col 47}{space 2} .0077361{col 58}{space 1}   -7.46{col 67}{space 3}0.000{col 75}{space 4}-.0730408{col 88}{space 3} -.042359
{txt}{space 33} {c |}
{space 28}_cons {c |}{col 35}{res}{space 2}-.0831537{col 47}{space 2} .4525382{col 58}{space 1}   -0.18{col 67}{space 3}0.855{col 75}{space 4} -.980554{col 88}{space 3} .8142466
{txt}{hline 34}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16} 2,507,103{col 28} -1943049{col 39} -1891516{col 50}    17{col 58}  3783065{col 69}  3783282
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] IC note}}.{p_end}

{com}. *
. ** BETWEEN-IDENTITY "IN" GROUP VERSUS "OUT" GROUP STATUS DIFFERENTIAL BETWEEN MINORITY/NON-MINORITY RESPONDENTS **
. 
. lincom c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0376959{col 26}{space 2} .0360387{col 37}{space 1}    1.05{col 46}{space 3}0.298{col 54}{space 4}-.0337702{col 67}{space 3} .1091619
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  1.minority#c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.minority#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0473003{col 26}{space 2} .0164833{col 37}{space 1}    2.87{col 46}{space 3}0.005{col 54}{space 4} .0146133{col 67}{space 3} .0799872
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. *
. *
. *
. 
.   
. *** MODEL G3.1: CONDITIONAL RESPONSES BY MINORITY WOMEN VERSUS WHITE WOMEN [BASELINE CATEGORY: MEN RESPONDENTS] -- GENDER BETWEEN-IDENTITY GROUP STATUS DIFFERENTIAL MODEL: [WOMEN SUPERVISORS WITHIN AGENCY j IN YEAR t / MEN SUPERVISORS WITHIN AGENCY j IN YEAR t] / [WOMEN NON-SUPERVISORS WITHIN AGENCY j IN YEAR t / MEN NON-SUPERVISORS WITHIN AGENCY j IN YEAR t]  -- CONTROLLING FOR GENDER SUPERVISORY EMPLOYEE IDENTITY GROUP DIFFERENTIAL ***
. 
. regress lndiversity2zeroadj  c.ln_ratio_fmsup_fmsub##i.women_het     minority  supervisor  topoffgender_2 lntotworkforce_count  ln_professionals_total_ratio   i.agencyid i.year, vce(cluster agencyid)

{txt}Linear regression                               Number of obs     = {res} 2,507,103
                                                {txt}{help j_robustsingular:F(18, 104) }       =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0406
                                                {txt}Root MSE          =    {res} .51446

{txt}{ralign 98:(Std. err. adjusted for {res:105} clusters in {res:agencyid})}
{hline 33}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 34}{c |}{col 46}    Robust
{col 1}             lndiversity2zeroadj{col 34}{c |} Coefficient{col 46}  std. err.{col 58}      t{col 66}   P>|t|{col 74}     [95% con{col 87}f. interval]
{hline 33}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}ln_ratio_fmsup_fmsub {c |}{col 34}{res}{space 2} .1519082{col 46}{space 2} .0421373{col 57}{space 1}    3.61{col 66}{space 3}0.000{col 74}{space 4} .0683482{col 87}{space 3} .2354681
{txt}{space 32} {c |}
{space 23}women_het {c |}
{space 30}1  {c |}{col 34}{res}{space 2} -.027855{col 46}{space 2} .0079006{col 57}{space 1}   -3.53{col 66}{space 3}0.001{col 74}{space 4}-.0435221{col 87}{space 3}-.0121878
{txt}{space 30}2  {c |}{col 34}{res}{space 2}-.0595642{col 46}{space 2} .0072268{col 57}{space 1}   -8.24{col 66}{space 3}0.000{col 74}{space 4}-.0738952{col 87}{space 3}-.0452332
{txt}{space 32} {c |}
women_het#c.ln_ratio_fmsup_fmsub {c |}
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{txt}{space 29}96  {c |}{col 34}{res}{space 2} .3345206{col 46}{space 2} .1447465{col 57}{space 1}    2.31{col 66}{space 3}0.023{col 74}{space 4} .0474828{col 87}{space 3} .6215584
{txt}{space 29}97  {c |}{col 34}{res}{space 2} .3871412{col 46}{space 2} .1259623{col 57}{space 1}    3.07{col 66}{space 3}0.003{col 74}{space 4} .1373532{col 87}{space 3} .6369293
{txt}{space 29}98  {c |}{col 34}{res}{space 2} .5533224{col 46}{space 2} .1786292{col 57}{space 1}    3.10{col 66}{space 3}0.003{col 74}{space 4}  .199094{col 87}{space 3} .9075508
{txt}{space 29}99  {c |}{col 34}{res}{space 2} .0811064{col 46}{space 2} .0246366{col 57}{space 1}    3.29{col 66}{space 3}0.001{col 74}{space 4} .0322511{col 87}{space 3} .1299617
{txt}{space 28}100  {c |}{col 34}{res}{space 2} .2500449{col 46}{space 2} .1473825{col 57}{space 1}    1.70{col 66}{space 3}0.093{col 74}{space 4}-.0422202{col 87}{space 3} .5423099
{txt}{space 28}101  {c |}{col 34}{res}{space 2} .3991407{col 46}{space 2} .1199291{col 57}{space 1}    3.33{col 66}{space 3}0.001{col 74}{space 4} .1613169{col 87}{space 3} .6369646
{txt}{space 28}102  {c |}{col 34}{res}{space 2} .2542417{col 46}{space 2} .1546162{col 57}{space 1}    1.64{col 66}{space 3}0.103{col 74}{space 4} -.052368{col 87}{space 3} .5608514
{txt}{space 28}103  {c |}{col 34}{res}{space 2} .0665668{col 46}{space 2} .0775049{col 57}{space 1}    0.86{col 66}{space 3}0.392{col 74}{space 4}-.0871284{col 87}{space 3}  .220262
{txt}{space 28}104  {c |}{col 34}{res}{space 2} -.108786{col 46}{space 2} .0397799{col 57}{space 1}   -2.73{col 66}{space 3}0.007{col 74}{space 4}-.1876709{col 87}{space 3}-.0299011
{txt}{space 28}105  {c |}{col 34}{res}{space 2} .3300227{col 46}{space 2} .1433195{col 57}{space 1}    2.30{col 66}{space 3}0.023{col 74}{space 4} .0458147{col 87}{space 3} .6142306
{txt}{space 32} {c |}
{space 28}year {c |}
{space 27}2011  {c |}{col 34}{res}{space 2}-.0040872{col 46}{space 2} .0032932{col 57}{space 1}   -1.24{col 66}{space 3}0.217{col 74}{space 4}-.0106177{col 87}{space 3} .0024432
{txt}{space 27}2012  {c |}{col 34}{res}{space 2}  .000083{col 46}{space 2} .0044579{col 57}{space 1}    0.02{col 66}{space 3}0.985{col 74}{space 4}-.0087573{col 87}{space 3} .0089233
{txt}{space 27}2013  {c |}{col 34}{res}{space 2}-.0009688{col 46}{space 2} .0046942{col 57}{space 1}   -0.21{col 66}{space 3}0.837{col 74}{space 4}-.0102775{col 87}{space 3} .0083398
{txt}{space 27}2014  {c |}{col 34}{res}{space 2}-.0069528{col 46}{space 2} .0064038{col 57}{space 1}   -1.09{col 66}{space 3}0.280{col 74}{space 4}-.0196518{col 87}{space 3} .0057461
{txt}{space 27}2015  {c |}{col 34}{res}{space 2}-.0145101{col 46}{space 2} .0078992{col 57}{space 1}   -1.84{col 66}{space 3}0.069{col 74}{space 4}-.0301745{col 87}{space 3} .0011544
{txt}{space 27}2016  {c |}{col 34}{res}{space 2}-.0177708{col 46}{space 2} .0067825{col 57}{space 1}   -2.62{col 66}{space 3}0.010{col 74}{space 4}-.0312208{col 87}{space 3}-.0043208
{txt}{space 27}2017  {c |}{col 34}{res}{space 2}-.0035505{col 46}{space 2} .0091464{col 57}{space 1}   -0.39{col 66}{space 3}0.699{col 74}{space 4}-.0216883{col 87}{space 3} .0145872
{txt}{space 27}2018  {c |}{col 34}{res}{space 2}-.0326412{col 46}{space 2} .0066958{col 57}{space 1}   -4.87{col 66}{space 3}0.000{col 74}{space 4}-.0459192{col 87}{space 3}-.0193632
{txt}{space 27}2019  {c |}{col 34}{res}{space 2}-.0709272{col 46}{space 2} .0080703{col 57}{space 1}   -8.79{col 66}{space 3}0.000{col 74}{space 4}-.0869309{col 87}{space 3}-.0549236
{txt}{space 32} {c |}
{space 27}_cons {c |}{col 34}{res}{space 2}-.1191411{col 46}{space 2} .4111215{col 57}{space 1}   -0.29{col 66}{space 3}0.773{col 74}{space 4}-.9344104{col 87}{space 3} .6961283
{txt}{hline 33}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16} 2,507,103{col 28} -1943049{col 39} -1891027{col 50}    19{col 58}  3782093{col 69}  3782335
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] IC note}}.{p_end}

{com}. *
. ** BETWEEN-IDENTITY "IN" GROUP VERSUS "OUT" GROUP STATUS DIFFERENTIAL BETWEEN GENDERED RESPONDENTS **
. 
. lincom c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .1519082{col 26}{space 2} .0421373{col 37}{space 1}    3.61{col 46}{space 3}0.000{col 54}{space 4} .0683482{col 67}{space 3} .2354681
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  1.women_het#c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.women_het#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.0033104{col 26}{space 2} .0209855{col 37}{space 1}   -0.16{col 46}{space 3}0.875{col 54}{space 4}-.0449255{col 67}{space 3} .0383047
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  2.women_het#c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}2.women_het#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0119886{col 26}{space 2}  .022471{col 37}{space 1}    0.53{col 46}{space 3}0.595{col 54}{space 4}-.0325723{col 67}{space 3} .0565494
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  2.women_het#c.ln_ratio_fmsup_fmsub -  1.women_het#c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1}{space 1}{res}- 1.women_het#c.ln_ratio_fmsup_fmsub + 2.women_het#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}  .015299{col 26}{space 2} .0181712{col 37}{space 1}    0.84{col 46}{space 3}0.402{col 54}{space 4}-.0207352{col 67}{space 3} .0513332
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. *
. 
. 
.    
. *** MODEL G4.1: CONDITIONAL RESPONSES BY MINORITY WOMEN VERSUS MINORITY MEN [BASELINE CATEGORY: NON-MINORITY RESPONDENTS] -- RACIAL/ETHNIC BETWEEN-IDENTITY GROUP STATUS DIFFERENTIAL MODEL: [MINORITY SUPERVISORS WITHIN AGENCY j IN YEAR t / NON-MINORITY SUPERVISORS WITHIN AGENCY j IN YEAR t] / [MINORITY NON-SUPERVISORS WITHIN AGENCY j IN YEAR t / NON-MINORITY NON-SUPERVISORS WITHIN AGENCY j IN YEAR t] -- CONTROLLING FOR RACIAL/ETHNIC SUPERVISORY EMPLOYEE IDENTITY GROUP DIFFERENTIAL ***
. 
. regress  lndiversity2zeroadj  c.ln_ratio_mnmsup_mnmsub##i.minority_het      gender supervisor  topoffminority_2 lntotworkforce_count  ln_professionals_total_ratio   i.agencyid i.year if e(sample), vce(cluster agencyid)

{txt}Linear regression                               Number of obs     = {res} 2,507,103
                                                {txt}{help j_robustsingular:F(18, 104) }       =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0405
                                                {txt}Root MSE          =    {res} .51448

{txt}{ralign 103:(Std. err. adjusted for {res:105} clusters in {res:agencyid})}
{hline 38}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 39}{c |}{col 51}    Robust
{col 1}                  lndiversity2zeroadj{col 39}{c |} Coefficient{col 51}  std. err.{col 63}      t{col 71}   P>|t|{col 79}     [95% con{col 92}f. interval]
{hline 38}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}ln_ratio_mnmsup_mnmsub {c |}{col 39}{res}{space 2} .0390987{col 51}{space 2} .0360778{col 62}{space 1}    1.08{col 71}{space 3}0.281{col 79}{space 4} -.032445{col 92}{space 3} .1106423
{txt}{space 37} {c |}
{space 25}minority_het {c |}
{space 35}1  {c |}{col 39}{res}{space 2}-.0677585{col 51}{space 2} .0056076{col 62}{space 1}  -12.08{col 71}{space 3}0.000{col 79}{space 4}-.0788786{col 92}{space 3}-.0566383
{txt}{space 35}2  {c |}{col 39}{res}{space 2}-.0943993{col 51}{space 2} .0069734{col 62}{space 1}  -13.54{col 71}{space 3}0.000{col 79}{space 4}-.1082279{col 92}{space 3}-.0805707
{txt}{space 37} {c |}
minority_het#c.ln_ratio_mnmsup_mnmsub {c |}
{space 35}1  {c |}{col 39}{res}{space 2} .0266902{col 51}{space 2} .0151249{col 62}{space 1}    1.76{col 71}{space 3}0.081{col 79}{space 4} -.003303{col 92}{space 3} .0566834
{txt}{space 35}2  {c |}{col 39}{res}{space 2}  .054359{col 51}{space 2} .0186152{col 62}{space 1}    2.92{col 71}{space 3}0.004{col 79}{space 4} .0174443{col 92}{space 3} .0912737
{txt}{space 37} {c |}
{space 31}gender {c |}{col 39}{res}{space 2} -.026941{col 51}{space 2} .0045157{col 62}{space 1}   -5.97{col 71}{space 3}0.000{col 79}{space 4}-.0358958{col 92}{space 3}-.0179863
{txt}{space 27}supervisor {c |}{col 39}{res}{space 2} .1273839{col 51}{space 2} .0073898{col 62}{space 1}   17.24{col 71}{space 3}0.000{col 79}{space 4} .1127296{col 92}{space 3} .1420381
{txt}{space 21}topoffminority_2 {c |}{col 39}{res}{space 2} .0075082{col 51}{space 2} .0058931{col 62}{space 1}    1.27{col 71}{space 3}0.205{col 79}{space 4}-.0041781{col 92}{space 3} .0191944
{txt}{space 17}lntotworkforce_count {c |}{col 39}{res}{space 2} .0691912{col 51}{space 2} .0373415{col 62}{space 1}    1.85{col 71}{space 3}0.067{col 79}{space 4}-.0048583{col 92}{space 3} .1432408
{txt}{space 9}ln_professionals_total_ratio {c |}{col 39}{res}{space 2} .0188091{col 51}{space 2} .0468611{col 62}{space 1}    0.40{col 71}{space 3}0.689{col 79}{space 4}-.0741182{col 92}{space 3} .1117364
{txt}{space 37} {c |}
{space 29}agencyid {c |}
{space 35}2  {c |}{col 39}{res}{space 2} .2207067{col 51}{space 2} .1175516{col 62}{space 1}    1.88{col 71}{space 3}0.063{col 79}{space 4}-.0124026{col 92}{space 3}  .453816
{txt}{space 35}3  {c |}{col 39}{res}{space 2} .0219256{col 51}{space 2} .0551407{col 62}{space 1}    0.40{col 71}{space 3}0.692{col 79}{space 4}-.0874205{col 92}{space 3} .1312716
{txt}{space 35}4  {c |}{col 39}{res}{space 2} .2708312{col 51}{space 2} .1533591{col 62}{space 1}    1.77{col 71}{space 3}0.080{col 79}{space 4}-.0332857{col 92}{space 3}  .574948
{txt}{space 35}5  {c |}{col 39}{res}{space 2} .2066981{col 51}{space 2} .1088337{col 62}{space 1}    1.90{col 71}{space 3}0.060{col 79}{space 4}-.0091232{col 92}{space 3} .4225194
{txt}{space 35}6  {c |}{col 39}{res}{space 2} .1800758{col 51}{space 2} .1163505{col 62}{space 1}    1.55{col 71}{space 3}0.125{col 79}{space 4}-.0506517{col 92}{space 3} .4108032
{txt}{space 35}7  {c |}{col 39}{res}{space 2} .3245708{col 51}{space 2} .1798115{col 62}{space 1}    1.81{col 71}{space 3}0.074{col 79}{space 4}-.0320023{col 92}{space 3} .6811438
{txt}{space 35}8  {c |}{col 39}{res}{space 2} .2682623{col 51}{space 2} .1461378{col 62}{space 1}    1.84{col 71}{space 3}0.069{col 79}{space 4}-.0215344{col 92}{space 3}  .558059
{txt}{space 35}9  {c |}{col 39}{res}{space 2} -.044554{col 51}{space 2} .0210262{col 62}{space 1}   -2.12{col 71}{space 3}0.036{col 79}{space 4}-.0862497{col 92}{space 3}-.0028583
{txt}{space 34}10  {c |}{col 39}{res}{space 2} .2536219{col 51}{space 2} .1958243{col 62}{space 1}    1.30{col 71}{space 3}0.198{col 79}{space 4} -.134705{col 92}{space 3} .6419489
{txt}{space 34}11  {c |}{col 39}{res}{space 2} .1966675{col 51}{space 2} .0865633{col 62}{space 1}    2.27{col 71}{space 3}0.025{col 79}{space 4} .0250092{col 92}{space 3} .3683257
{txt}{space 34}12  {c |}{col 39}{res}{space 2} .3302825{col 51}{space 2} .1963276{col 62}{space 1}    1.68{col 71}{space 3}0.096{col 79}{space 4}-.0590426{col 92}{space 3} .7196075
{txt}{space 34}13  {c |}{col 39}{res}{space 2} .3215096{col 51}{space 2} .1581214{col 62}{space 1}    2.03{col 71}{space 3}0.045{col 79}{space 4} .0079489{col 92}{space 3} .6350703
{txt}{space 34}14  {c |}{col 39}{res}{space 2} .1469887{col 51}{space 2} .1111855{col 62}{space 1}    1.32{col 71}{space 3}0.189{col 79}{space 4}-.0734963{col 92}{space 3} .3674736
{txt}{space 34}15  {c |}{col 39}{res}{space 2} .3121034{col 51}{space 2} .1214453{col 62}{space 1}    2.57{col 71}{space 3}0.012{col 79}{space 4} .0712728{col 92}{space 3}  .552934
{txt}{space 34}16  {c |}{col 39}{res}{space 2} .3929599{col 51}{space 2} .1940544{col 62}{space 1}    2.02{col 71}{space 3}0.045{col 79}{space 4} .0081427{col 92}{space 3} .7777771
{txt}{space 34}17  {c |}{col 39}{res}{space 2} .1118789{col 51}{space 2} .1549861{col 62}{space 1}    0.72{col 71}{space 3}0.472{col 79}{space 4}-.1954643{col 92}{space 3}  .419222
{txt}{space 34}18  {c |}{col 39}{res}{space 2} .3505387{col 51}{space 2} .1544381{col 62}{space 1}    2.27{col 71}{space 3}0.025{col 79}{space 4}  .044282{col 92}{space 3} .6567953
{txt}{space 34}19  {c |}{col 39}{res}{space 2} .2029892{col 51}{space 2} .1050274{col 62}{space 1}    1.93{col 71}{space 3}0.056{col 79}{space 4}-.0052841{col 92}{space 3} .4112624
{txt}{space 34}20  {c |}{col 39}{res}{space 2} .0867249{col 51}{space 2} .1112541{col 62}{space 1}    0.78{col 71}{space 3}0.437{col 79}{space 4}-.1338963{col 92}{space 3}  .307346
{txt}{space 34}21  {c |}{col 39}{res}{space 2} .2356947{col 51}{space 2} .0978069{col 62}{space 1}    2.41{col 71}{space 3}0.018{col 79}{space 4} .0417399{col 92}{space 3} .4296495
{txt}{space 34}22  {c |}{col 39}{res}{space 2} .2109566{col 51}{space 2} .1457689{col 62}{space 1}    1.45{col 71}{space 3}0.151{col 79}{space 4}-.0781085{col 92}{space 3} .5000218
{txt}{space 34}23  {c |}{col 39}{res}{space 2}-.0491691{col 51}{space 2} .0616276{col 62}{space 1}   -0.80{col 71}{space 3}0.427{col 79}{space 4}-.1713788{col 92}{space 3} .0730407
{txt}{space 34}24  {c |}{col 39}{res}{space 2} .2654018{col 51}{space 2} .1118641{col 62}{space 1}    2.37{col 71}{space 3}0.020{col 79}{space 4} .0435712{col 92}{space 3} .4872325
{txt}{space 34}25  {c |}{col 39}{res}{space 2} .2305969{col 51}{space 2} .1254166{col 62}{space 1}    1.84{col 71}{space 3}0.069{col 79}{space 4}-.0181089{col 92}{space 3} .4793028
{txt}{space 34}26  {c |}{col 39}{res}{space 2} .0775786{col 51}{space 2}  .113918{col 62}{space 1}    0.68{col 71}{space 3}0.497{col 79}{space 4}-.1483251{col 92}{space 3} .3034822
{txt}{space 34}27  {c |}{col 39}{res}{space 2} .3551901{col 51}{space 2} .1866059{col 62}{space 1}    1.90{col 71}{space 3}0.060{col 79}{space 4}-.0148563{col 92}{space 3} .7252366
{txt}{space 34}28  {c |}{col 39}{res}{space 2}-.0081379{col 51}{space 2} .0897189{col 62}{space 1}   -0.09{col 71}{space 3}0.928{col 79}{space 4}-.1860538{col 92}{space 3}  .169778
{txt}{space 34}29  {c |}{col 39}{res}{space 2} .1179077{col 51}{space 2} .1631392{col 62}{space 1}    0.72{col 71}{space 3}0.471{col 79}{space 4}-.2056035{col 92}{space 3} .4414189
{txt}{space 34}30  {c |}{col 39}{res}{space 2} .1790078{col 51}{space 2} .1540854{col 62}{space 1}    1.16{col 71}{space 3}0.248{col 79}{space 4}-.1265493{col 92}{space 3}  .484565
{txt}{space 34}31  {c |}{col 39}{res}{space 2} .0269182{col 51}{space 2} .1590131{col 62}{space 1}    0.17{col 71}{space 3}0.866{col 79}{space 4}-.2884108{col 92}{space 3} .3422472
{txt}{space 34}32  {c |}{col 39}{res}{space 2} .2405135{col 51}{space 2} .1253317{col 62}{space 1}    1.92{col 71}{space 3}0.058{col 79}{space 4}-.0080239{col 92}{space 3}  .489051
{txt}{space 34}33  {c |}{col 39}{res}{space 2} .1559944{col 51}{space 2} .0717589{col 62}{space 1}    2.17{col 71}{space 3}0.032{col 79}{space 4} .0136939{col 92}{space 3} .2982949
{txt}{space 34}34  {c |}{col 39}{res}{space 2}  .047187{col 51}{space 2} .1124926{col 62}{space 1}    0.42{col 71}{space 3}0.676{col 79}{space 4}  -.17589{col 92}{space 3}  .270264
{txt}{space 34}35  {c |}{col 39}{res}{space 2} .1257594{col 51}{space 2} .1019451{col 62}{space 1}    1.23{col 71}{space 3}0.220{col 79}{space 4}-.0764017{col 92}{space 3} .3279204
{txt}{space 34}36  {c |}{col 39}{res}{space 2} .2162575{col 51}{space 2} .1303886{col 62}{space 1}    1.66{col 71}{space 3}0.100{col 79}{space 4}-.0423079{col 92}{space 3}  .474823
{txt}{space 34}37  {c |}{col 39}{res}{space 2} .1826049{col 51}{space 2} .1163037{col 62}{space 1}    1.57{col 71}{space 3}0.119{col 79}{space 4}-.0480297{col 92}{space 3} .4132396
{txt}{space 34}38  {c |}{col 39}{res}{space 2}  .355132{col 51}{space 2} .1767855{col 62}{space 1}    2.01{col 71}{space 3}0.047{col 79}{space 4} .0045597{col 92}{space 3} .7057043
{txt}{space 34}39  {c |}{col 39}{res}{space 2} .1854989{col 51}{space 2} .1213229{col 62}{space 1}    1.53{col 71}{space 3}0.129{col 79}{space 4} -.055089{col 92}{space 3} .4260869
{txt}{space 34}40  {c |}{col 39}{res}{space 2} .0258756{col 51}{space 2} .0753344{col 62}{space 1}    0.34{col 71}{space 3}0.732{col 79}{space 4}-.1235153{col 92}{space 3} .1752665
{txt}{space 34}41  {c |}{col 39}{res}{space 2} .2444219{col 51}{space 2} .1431957{col 62}{space 1}    1.71{col 71}{space 3}0.091{col 79}{space 4}-.0395404{col 92}{space 3} .5283843
{txt}{space 34}42  {c |}{col 39}{res}{space 2} .2359686{col 51}{space 2} .1443125{col 62}{space 1}    1.64{col 71}{space 3}0.105{col 79}{space 4}-.0502085{col 92}{space 3} .5221457
{txt}{space 34}43  {c |}{col 39}{res}{space 2} .0420158{col 51}{space 2} .0562092{col 62}{space 1}    0.75{col 71}{space 3}0.456{col 79}{space 4}-.0694491{col 92}{space 3} .1534806
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{txt}{space 34}82  {c |}{col 39}{res}{space 2} .2862192{col 51}{space 2} .1832259{col 62}{space 1}    1.56{col 71}{space 3}0.121{col 79}{space 4}-.0771246{col 92}{space 3}  .649563
{txt}{space 34}83  {c |}{col 39}{res}{space 2} .3798131{col 51}{space 2} .1557567{col 62}{space 1}    2.44{col 71}{space 3}0.016{col 79}{space 4} .0709418{col 92}{space 3} .6886844
{txt}{space 34}84  {c |}{col 39}{res}{space 2} .3446325{col 51}{space 2} .1830916{col 62}{space 1}    1.88{col 71}{space 3}0.063{col 79}{space 4}-.0184449{col 92}{space 3}   .70771
{txt}{space 34}85  {c |}{col 39}{res}{space 2} .3400538{col 51}{space 2} .1396996{col 62}{space 1}    2.43{col 71}{space 3}0.017{col 79}{space 4} .0630241{col 92}{space 3} .6170834
{txt}{space 34}86  {c |}{col 39}{res}{space 2} .4244665{col 51}{space 2} .1981522{col 62}{space 1}    2.14{col 71}{space 3}0.035{col 79}{space 4} .0315233{col 92}{space 3} .8174096
{txt}{space 34}87  {c |}{col 39}{res}{space 2} .4485297{col 51}{space 2} .2070237{col 62}{space 1}    2.17{col 71}{space 3}0.033{col 79}{space 4}  .037994{col 92}{space 3} .8590654
{txt}{space 34}88  {c |}{col 39}{res}{space 2} .2279808{col 51}{space 2} .1396583{col 62}{space 1}    1.63{col 71}{space 3}0.106{col 79}{space 4}-.0489668{col 92}{space 3} .5049284
{txt}{space 34}89  {c |}{col 39}{res}{space 2} .2631965{col 51}{space 2} .1520272{col 62}{space 1}    1.73{col 71}{space 3}0.086{col 79}{space 4}-.0382791{col 92}{space 3} .5646721
{txt}{space 34}90  {c |}{col 39}{res}{space 2}  .086595{col 51}{space 2} .0493654{col 62}{space 1}    1.75{col 71}{space 3}0.082{col 79}{space 4}-.0112984{col 92}{space 3} .1844884
{txt}{space 34}91  {c |}{col 39}{res}{space 2} .1820132{col 51}{space 2} .1005196{col 62}{space 1}    1.81{col 71}{space 3}0.073{col 79}{space 4}-.0173208{col 92}{space 3} .3813473
{txt}{space 34}92  {c |}{col 39}{res}{space 2} .0864329{col 51}{space 2} .0539583{col 62}{space 1}    1.60{col 71}{space 3}0.112{col 79}{space 4}-.0205685{col 92}{space 3} .1934342
{txt}{space 34}93  {c |}{col 39}{res}{space 2} .3882544{col 51}{space 2} .1590101{col 62}{space 1}    2.44{col 71}{space 3}0.016{col 79}{space 4} .0729313{col 92}{space 3} .7035775
{txt}{space 34}94  {c |}{col 39}{res}{space 2}  .463014{col 51}{space 2} .1990076{col 62}{space 1}    2.33{col 71}{space 3}0.022{col 79}{space 4} .0683744{col 92}{space 3} .8576536
{txt}{space 34}95  {c |}{col 39}{res}{space 2} .2476263{col 51}{space 2} .1697145{col 62}{space 1}    1.46{col 71}{space 3}0.148{col 79}{space 4} -.088924{col 92}{space 3} .5841765
{txt}{space 34}96  {c |}{col 39}{res}{space 2} .3232105{col 51}{space 2}  .173014{col 62}{space 1}    1.87{col 71}{space 3}0.065{col 79}{space 4}-.0198827{col 92}{space 3} .6663037
{txt}{space 34}97  {c |}{col 39}{res}{space 2} .3451967{col 51}{space 2} .1430661{col 62}{space 1}    2.41{col 71}{space 3}0.018{col 79}{space 4} .0614913{col 92}{space 3} .6289021
{txt}{space 34}98  {c |}{col 39}{res}{space 2} .4809601{col 51}{space 2} .2033275{col 62}{space 1}    2.37{col 71}{space 3}0.020{col 79}{space 4}  .077754{col 92}{space 3} .8841661
{txt}{space 34}99  {c |}{col 39}{res}{space 2} .0925216{col 51}{space 2} .0273431{col 62}{space 1}    3.38{col 71}{space 3}0.001{col 79}{space 4} .0382992{col 92}{space 3} .1467441
{txt}{space 33}100  {c |}{col 39}{res}{space 2} .2711155{col 51}{space 2} .1780261{col 62}{space 1}    1.52{col 71}{space 3}0.131{col 79}{space 4}-.0819169{col 92}{space 3}  .624148
{txt}{space 33}101  {c |}{col 39}{res}{space 2} .3981802{col 51}{space 2} .1477318{col 62}{space 1}    2.70{col 71}{space 3}0.008{col 79}{space 4} .1052226{col 92}{space 3} .6911378
{txt}{space 33}102  {c |}{col 39}{res}{space 2}  .317402{col 51}{space 2} .1875552{col 62}{space 1}    1.69{col 71}{space 3}0.094{col 79}{space 4}-.0545269{col 92}{space 3} .6893309
{txt}{space 33}103  {c |}{col 39}{res}{space 2} .1242181{col 51}{space 2} .0944038{col 62}{space 1}    1.32{col 71}{space 3}0.191{col 79}{space 4}-.0629882{col 92}{space 3} .3114244
{txt}{space 33}104  {c |}{col 39}{res}{space 2}-.1237686{col 51}{space 2}  .049994{col 62}{space 1}   -2.48{col 71}{space 3}0.015{col 79}{space 4}-.2229086{col 92}{space 3}-.0246287
{txt}{space 33}105  {c |}{col 39}{res}{space 2} .2870961{col 51}{space 2} .1702077{col 62}{space 1}    1.69{col 71}{space 3}0.095{col 79}{space 4} -.050432{col 92}{space 3} .6246243
{txt}{space 37} {c |}
{space 33}year {c |}
{space 32}2011  {c |}{col 39}{res}{space 2}-.0017643{col 51}{space 2}  .003946{col 62}{space 1}   -0.45{col 71}{space 3}0.656{col 79}{space 4}-.0095894{col 92}{space 3} .0060609
{txt}{space 32}2012  {c |}{col 39}{res}{space 2}  .006467{col 51}{space 2} .0048738{col 62}{space 1}    1.33{col 71}{space 3}0.187{col 79}{space 4}-.0031979{col 92}{space 3} .0161319
{txt}{space 32}2013  {c |}{col 39}{res}{space 2} .0056359{col 51}{space 2} .0057528{col 62}{space 1}    0.98{col 71}{space 3}0.330{col 79}{space 4}-.0057721{col 92}{space 3} .0170439
{txt}{space 32}2014  {c |}{col 39}{res}{space 2} .0017864{col 51}{space 2} .0062758{col 62}{space 1}    0.28{col 71}{space 3}0.776{col 79}{space 4}-.0106586{col 92}{space 3} .0142315
{txt}{space 32}2015  {c |}{col 39}{res}{space 2} -.004757{col 51}{space 2} .0071754{col 62}{space 1}   -0.66{col 71}{space 3}0.509{col 79}{space 4}-.0189861{col 92}{space 3} .0094721
{txt}{space 32}2016  {c |}{col 39}{res}{space 2}-.0063374{col 51}{space 2} .0063704{col 62}{space 1}   -0.99{col 71}{space 3}0.322{col 79}{space 4}-.0189702{col 92}{space 3} .0062954
{txt}{space 32}2017  {c |}{col 39}{res}{space 2} .0056484{col 51}{space 2} .0098032{col 62}{space 1}    0.58{col 71}{space 3}0.566{col 79}{space 4}-.0137917{col 92}{space 3} .0250885
{txt}{space 32}2018  {c |}{col 39}{res}{space 2}-.0196879{col 51}{space 2} .0069207{col 62}{space 1}   -2.84{col 71}{space 3}0.005{col 79}{space 4}-.0334119{col 92}{space 3}-.0059639
{txt}{space 32}2019  {c |}{col 39}{res}{space 2}-.0577463{col 51}{space 2} .0077232{col 62}{space 1}   -7.48{col 71}{space 3}0.000{col 79}{space 4}-.0730617{col 92}{space 3} -.042431
{txt}{space 37} {c |}
{space 32}_cons {c |}{col 39}{res}{space 2}-.0836771{col 51}{space 2} .4530762{col 62}{space 1}   -0.18{col 71}{space 3}0.854{col 79}{space 4}-.9821442{col 92}{space 3} .8147901
{txt}{hline 38}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16} 2,507,103{col 28} -1943049{col 39} -1891166{col 50}    19{col 58}  3782369{col 69}  3782611
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] IC note}}.{p_end}

{com}. *
. ** BETWEEN-IDENTITY "IN" GROUP VERSUS "OUT" GROUP STATUS DIFFERENTIAL BETWEEN MINORITY/NON-MINORITY RESPONDENTS **
. 
. lincom c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0390987{col 26}{space 2} .0360778{col 37}{space 1}    1.08{col 46}{space 3}0.281{col 54}{space 4} -.032445{col 67}{space 3} .1106423
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  1.minority_het#c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.minority_het#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0266902{col 26}{space 2} .0151249{col 37}{space 1}    1.76{col 46}{space 3}0.081{col 54}{space 4} -.003303{col 67}{space 3} .0566834
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  2.minority_het#c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}2.minority_het#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}  .054359{col 26}{space 2} .0186152{col 37}{space 1}    2.92{col 46}{space 3}0.004{col 54}{space 4} .0174443{col 67}{space 3} .0912737
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  2.minority_het#c.ln_ratio_mnmsup_mnmsub -  1.minority_het#c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1}{space 1}{res}- 1.minority_het#c.ln_ratio_mnmsup_mnmsub + 2.minority_het#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0276687{col 26}{space 2} .0131277{col 37}{space 1}    2.11{col 46}{space 3}0.037{col 54}{space 4} .0016361{col 67}{space 3} .0537014
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. *
. 
. **********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
.    
. *** 3. CONDITIONAL-RESPONDENT MODELS EVALUATING THE RELATIONSHIP INVOLVING WITHIN-IDENTITY "OUT-GROUP" STATUS & BETWEEN-IDENTITY GROUP STATUS DIFFERENTIALS AS A MEANS TO FOSTER DIVERSITY AND INCLUSION IN THE U.S. CIVILIAN WORKFORCE ///
> ***             [BY NON-SUPERVISORS POSITIONS VERSUS SUPERVISORY POSITION] ***   
. 
. 
.    
. *** MODEL G5.1: CONDITIONAL RESPONSES BY GENDER & POSITION --  GENDER WITHIN-IDENTITY 'OUT-GROUP' STATUS DIFFERENTIAL MODEL: [WOMEN SUPERVISORS WITHIN AGENCY j IN YEAR t / WOMEN NON-SUPERVISORS WITHIN AGENCY j IN YEAR t] -- CONTROLLING FOR GENDER SUPERVISORY EMPLOYEE IDENTITY GROUP DIFFERENTIAL ***
. 
. regress  lndiversity2zeroadj  c.ln_ratio_fmsup_fmsub##i.gender##i.supervisor      minority  topoffgender_2 lntotworkforce_count  ln_professionals_total_ratio   i.agencyid i.year, vce(cluster agencyid)

{txt}Linear regression                               Number of obs     = {res} 2,507,103
                                                {txt}{help j_robustsingular:F(19, 104) }       =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0405
                                                {txt}Root MSE          =    {res} .51451

{txt}{ralign 106:(Std. err. adjusted for {res:105} clusters in {res:agencyid})}
{hline 41}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 42}{c |}{col 54}    Robust
{col 1}                     lndiversity2zeroadj{col 42}{c |} Coefficient{col 54}  std. err.{col 66}      t{col 74}   P>|t|{col 82}     [95% con{col 95}f. interval]
{hline 41}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}ln_ratio_fmsup_fmsub {c |}{col 42}{res}{space 2} .1373082{col 54}{space 2} .0450462{col 65}{space 1}    3.05{col 74}{space 3}0.003{col 82}{space 4}   .04798{col 95}{space 3} .2266365
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{txt}{space 40} {c |}
{space 11}gender#c.ln_ratio_fmsup_fmsub {c |}
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{txt}{space 40} {c |}
{space 28}1.supervisor {c |}{col 42}{res}{space 2} .1446199{col 54}{space 2} .0209991{col 65}{space 1}    6.89{col 74}{space 3}0.000{col 82}{space 4} .1029779{col 95}{space 3} .1862619
{txt}{space 40} {c |}
{space 7}supervisor#c.ln_ratio_fmsup_fmsub {c |}
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{txt}{space 40} {c |}
{space 23}gender#supervisor {c |}
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{txt}{space 40} {c |}
gender#supervisor#c.ln_ratio_fmsup_fmsub {c |}
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{txt}{space 40} {c |}
{space 32}minority {c |}{col 42}{res}{space 2}-.0944401{col 54}{space 2} .0037705{col 65}{space 1}  -25.05{col 74}{space 3}0.000{col 82}{space 4}-.1019171{col 95}{space 3} -.086963
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{txt}{space 40} {c |}
{space 32}agencyid {c |}
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{txt}{space 37}89  {c |}{col 42}{res}{space 2} .2979684{col 54}{space 2} .1399802{col 65}{space 1}    2.13{col 74}{space 3}0.036{col 82}{space 4} .0203825{col 95}{space 3} .5755543
{txt}{space 37}90  {c |}{col 42}{res}{space 2} .0705139{col 54}{space 2} .0415802{col 65}{space 1}    1.70{col 74}{space 3}0.093{col 82}{space 4}-.0119413{col 95}{space 3}  .152969
{txt}{space 37}91  {c |}{col 42}{res}{space 2} .1907566{col 54}{space 2} .0902515{col 65}{space 1}    2.11{col 74}{space 3}0.037{col 82}{space 4} .0117845{col 95}{space 3} .3697287
{txt}{space 37}92  {c |}{col 42}{res}{space 2} .0823903{col 54}{space 2} .0454054{col 65}{space 1}    1.81{col 74}{space 3}0.072{col 82}{space 4}-.0076503{col 95}{space 3}  .172431
{txt}{space 37}93  {c |}{col 42}{res}{space 2} .4303219{col 54}{space 2} .1435757{col 65}{space 1}    3.00{col 74}{space 3}0.003{col 82}{space 4} .1456058{col 95}{space 3} .7150379
{txt}{space 37}94  {c |}{col 42}{res}{space 2} .4679306{col 54}{space 2} .1660165{col 65}{space 1}    2.82{col 74}{space 3}0.006{col 82}{space 4} .1387137{col 95}{space 3} .7971475
{txt}{space 37}95  {c |}{col 42}{res}{space 2} .2235531{col 54}{space 2} .1444514{col 65}{space 1}    1.55{col 74}{space 3}0.125{col 82}{space 4}-.0628995{col 95}{space 3} .5100057
{txt}{space 37}96  {c |}{col 42}{res}{space 2} .3405788{col 54}{space 2} .1437118{col 65}{space 1}    2.37{col 74}{space 3}0.020{col 82}{space 4} .0555929{col 95}{space 3} .6255646
{txt}{space 37}97  {c |}{col 42}{res}{space 2} .3937774{col 54}{space 2} .1250251{col 65}{space 1}    3.15{col 74}{space 3}0.002{col 82}{space 4} .1458479{col 95}{space 3} .6417068
{txt}{space 37}98  {c |}{col 42}{res}{space 2} .5601265{col 54}{space 2} .1773078{col 65}{space 1}    3.16{col 74}{space 3}0.002{col 82}{space 4} .2085185{col 95}{space 3} .9117346
{txt}{space 37}99  {c |}{col 42}{res}{space 2} .0822952{col 54}{space 2} .0245497{col 65}{space 1}    3.35{col 74}{space 3}0.001{col 82}{space 4} .0336121{col 95}{space 3} .1309783
{txt}{space 36}100  {c |}{col 42}{res}{space 2} .2562285{col 54}{space 2} .1463862{col 65}{space 1}    1.75{col 74}{space 3}0.083{col 82}{space 4}-.0340608{col 95}{space 3} .5465179
{txt}{space 36}101  {c |}{col 42}{res}{space 2} .4042661{col 54}{space 2} .1190083{col 65}{space 1}    3.40{col 74}{space 3}0.001{col 82}{space 4} .1682681{col 95}{space 3}  .640264
{txt}{space 36}102  {c |}{col 42}{res}{space 2} .2563713{col 54}{space 2} .1542451{col 65}{space 1}    1.66{col 74}{space 3}0.100{col 82}{space 4}-.0495027{col 95}{space 3} .5622452
{txt}{space 36}103  {c |}{col 42}{res}{space 2} .0703008{col 54}{space 2} .0769374{col 65}{space 1}    0.91{col 74}{space 3}0.363{col 82}{space 4}-.0822689{col 95}{space 3} .2228705
{txt}{space 36}104  {c |}{col 42}{res}{space 2}-.1078111{col 54}{space 2} .0394643{col 65}{space 1}   -2.73{col 74}{space 3}0.007{col 82}{space 4}-.1860703{col 95}{space 3}-.0295519
{txt}{space 36}105  {c |}{col 42}{res}{space 2} .3347631{col 54}{space 2} .1422883{col 65}{space 1}    2.35{col 74}{space 3}0.021{col 82}{space 4} .0526001{col 95}{space 3} .6169261
{txt}{space 40} {c |}
{space 36}year {c |}
{space 35}2011  {c |}{col 42}{res}{space 2}-.0040554{col 54}{space 2} .0032918{col 65}{space 1}   -1.23{col 74}{space 3}0.221{col 82}{space 4}-.0105831{col 95}{space 3} .0024723
{txt}{space 35}2012  {c |}{col 42}{res}{space 2}-.0000994{col 54}{space 2} .0045143{col 65}{space 1}   -0.02{col 74}{space 3}0.982{col 82}{space 4}-.0090514{col 95}{space 3} .0088527
{txt}{space 35}2013  {c |}{col 42}{res}{space 2}-.0011706{col 54}{space 2} .0047725{col 65}{space 1}   -0.25{col 74}{space 3}0.807{col 82}{space 4}-.0106346{col 95}{space 3} .0082935
{txt}{space 35}2014  {c |}{col 42}{res}{space 2}-.0071342{col 54}{space 2} .0065024{col 65}{space 1}   -1.10{col 74}{space 3}0.275{col 82}{space 4}-.0200286{col 95}{space 3} .0057603
{txt}{space 35}2015  {c |}{col 42}{res}{space 2} -.014658{col 54}{space 2} .0079479{col 65}{space 1}   -1.84{col 74}{space 3}0.068{col 82}{space 4}-.0304191{col 95}{space 3}  .001103
{txt}{space 35}2016  {c |}{col 42}{res}{space 2}-.0179117{col 54}{space 2} .0068142{col 65}{space 1}   -2.63{col 74}{space 3}0.010{col 82}{space 4}-.0314245{col 95}{space 3}-.0043989
{txt}{space 35}2017  {c |}{col 42}{res}{space 2}-.0036416{col 54}{space 2} .0091241{col 65}{space 1}   -0.40{col 74}{space 3}0.691{col 82}{space 4} -.021735{col 95}{space 3} .0144518
{txt}{space 35}2018  {c |}{col 42}{res}{space 2}-.0327559{col 54}{space 2} .0067284{col 65}{space 1}   -4.87{col 74}{space 3}0.000{col 82}{space 4}-.0460986{col 95}{space 3}-.0194132
{txt}{space 35}2019  {c |}{col 42}{res}{space 2}-.0710986{col 54}{space 2} .0080914{col 65}{space 1}   -8.79{col 74}{space 3}0.000{col 82}{space 4}-.0871442{col 95}{space 3}-.0550531
{txt}{space 40} {c |}
{space 35}_cons {c |}{col 42}{res}{space 2}-.1350335{col 54}{space 2} .4076854{col 65}{space 1}   -0.33{col 74}{space 3}0.741{col 82}{space 4}-.9434888{col 95}{space 3} .6734219
{txt}{hline 41}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16} 2,507,103{col 28} -1943049{col 39} -1891278{col 50}    20{col 58}  3782597{col 69}  3782851
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] IC note}}.{p_end}

{com}. *
. ** BY NON-SUPERVISORS RESPONDENT: WITHIN-IDENTITY "OUT" GROUP STATUS DIFFERENTIAL BETWEEN GENDERED RESPONDENTS **
. 
. lincom c.ln_ratio_fmsup_fmsub 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .1373082{col 26}{space 2} .0450462{col 37}{space 1}    3.05{col 46}{space 3}0.003{col 54}{space 4}   .04798{col 67}{space 3} .2266365
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  1.gender#c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.gender#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0075226{col 26}{space 2} .0262664{col 37}{space 1}    0.29{col 46}{space 3}0.775{col 54}{space 4}-.0445646{col 67}{space 3} .0596099
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *
. *
. *
. *
. 
. ** BY SUPERVISOR RESPONDENT: WITHIN-IDENTITY "OUT" GROUP STATUS DIFFERENTIAL BETWEEN GENDERED RESPONDENTS **
. 
. lincom c.ln_ratio_fmsup_fmsub + 1.supervisor#c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}ln_ratio_fmsup_fmsub + 1.supervisor#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .1946912{col 26}{space 2} .0488637{col 37}{space 1}    3.98{col 46}{space 3}0.000{col 54}{space 4} .0977927{col 67}{space 3} .2915898
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  1.gender#c.ln_ratio_fmsup_fmsub +  1.gender#1.supervisor#c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.gender#c.ln_ratio_fmsup_fmsub + 1.gender#1.supervisor#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0015738{col 26}{space 2} .0187407{col 37}{space 1}    0.08{col 46}{space 3}0.933{col 54}{space 4}-.0355898{col 67}{space 3} .0387374
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. 
. 
. 
. *** MODEL G6.1: CONDITIONAL RESPONSES BY RACE/ETHNICITIY & POSITION -- RACIAL/ETHNIC WITHIN-'OUT-GROUP' STATUS DIFFERENTIAL MODEL: [MINORITY SUPERVISORS WITHIN AGENCY j IN YEAR t / NON-MINORITY NON-SUPERVISORS WITHIN AGENCY j IN YEAR t]  -- CONTROLLING FOR RACIAL/ETHNIC SUPERVISORY EMPLOYEE IDENTITY GROUP DIFFERENTIAL ***
. 
. regress lndiversity2zeroadj  c.ln_ratio_mnmsup_mnmsub##i.minority##i.supervisor     gender  topoffminority_2 lntotworkforce_count  ln_professionals_total_ratio   i.agencyid i.year, vce(cluster agencyid)

{txt}Linear regression                               Number of obs     = {res} 2,507,103
                                                {txt}{help j_robustsingular:F(19, 104) }       =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0403
                                                {txt}Root MSE          =    {res} .51455

{txt}{ralign 110:(Std. err. adjusted for {res:105} clusters in {res:agencyid})}
{hline 45}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 46}{c |}{col 58}    Robust
{col 1}                         lndiversity2zeroadj{col 46}{c |} Coefficient{col 58}  std. err.{col 70}      t{col 78}   P>|t|{col 86}     [95% con{col 99}f. interval]
{hline 45}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}ln_ratio_mnmsup_mnmsub {c |}{col 46}{res}{space 2} .0350634{col 58}{space 2} .0368458{col 69}{space 1}    0.95{col 78}{space 3}0.343{col 86}{space 4}-.0380032{col 99}{space 3} .1081301
{txt}{space 34}1.minority {c |}{col 46}{res}{space 2}-.0814914{col 58}{space 2} .0069999{col 69}{space 1}  -11.64{col 78}{space 3}0.000{col 86}{space 4}-.0953725{col 99}{space 3}-.0676102
{txt}{space 44} {c |}
{space 11}minority#c.ln_ratio_mnmsup_mnmsub {c |}
{space 42}1  {c |}{col 46}{res}{space 2} .0421782{col 58}{space 2} .0188612{col 69}{space 1}    2.24{col 78}{space 3}0.027{col 86}{space 4} .0047757{col 99}{space 3} .0795807
{txt}{space 44} {c |}
{space 32}1.supervisor {c |}{col 46}{res}{space 2} .1284181{col 58}{space 2} .0135732{col 69}{space 1}    9.46{col 78}{space 3}0.000{col 86}{space 4} .1015019{col 99}{space 3} .1553343
{txt}{space 44} {c |}
{space 9}supervisor#c.ln_ratio_mnmsup_mnmsub {c |}
{space 42}1  {c |}{col 46}{res}{space 2} .0100522{col 58}{space 2} .0269531{col 69}{space 1}    0.37{col 78}{space 3}0.710{col 86}{space 4}-.0433969{col 99}{space 3} .0635013
{txt}{space 44} {c |}
{space 25}minority#supervisor {c |}
{space 40}1 1  {c |}{col 46}{res}{space 2} .0162987{col 58}{space 2}  .008549{col 69}{space 1}    1.91{col 78}{space 3}0.059{col 86}{space 4}-.0006544{col 99}{space 3} .0332517
{txt}{space 44} {c |}
minority#supervisor#c.ln_ratio_mnmsup_mnmsub {c |}
{space 40}1 1  {c |}{col 46}{res}{space 2} .0273166{col 58}{space 2} .0174419{col 69}{space 1}    1.57{col 78}{space 3}0.120{col 86}{space 4}-.0072713{col 99}{space 3} .0619045
{txt}{space 44} {c |}
{space 38}gender {c |}{col 46}{res}{space 2} -.039538{col 58}{space 2} .0041582{col 69}{space 1}   -9.51{col 78}{space 3}0.000{col 86}{space 4}-.0477839{col 99}{space 3} -.031292
{txt}{space 28}topoffminority_2 {c |}{col 46}{res}{space 2} .0074869{col 58}{space 2} .0058769{col 69}{space 1}    1.27{col 78}{space 3}0.206{col 86}{space 4}-.0041672{col 99}{space 3}  .019141
{txt}{space 24}lntotworkforce_count {c |}{col 46}{res}{space 2} .0693851{col 58}{space 2}  .037326{col 69}{space 1}    1.86{col 78}{space 3}0.066{col 86}{space 4}-.0046336{col 99}{space 3} .1434039
{txt}{space 16}ln_professionals_total_ratio {c |}{col 46}{res}{space 2} .0190822{col 58}{space 2} .0467284{col 69}{space 1}    0.41{col 78}{space 3}0.684{col 86}{space 4}-.0735819{col 99}{space 3} .1117464
{txt}{space 44} {c |}
{space 36}agencyid {c |}
{space 42}2  {c |}{col 46}{res}{space 2} .2216547{col 58}{space 2} .1174898{col 69}{space 1}    1.89{col 78}{space 3}0.062{col 86}{space 4} -.011332{col 99}{space 3} .4546413
{txt}{space 42}3  {c |}{col 46}{res}{space 2} .0216885{col 58}{space 2} .0553436{col 69}{space 1}    0.39{col 78}{space 3}0.696{col 86}{space 4}-.0880599{col 99}{space 3} .1314369
{txt}{space 42}4  {c |}{col 46}{res}{space 2} .2730647{col 58}{space 2} .1531422{col 69}{space 1}    1.78{col 78}{space 3}0.077{col 86}{space 4} -.030622{col 99}{space 3} .5767514
{txt}{space 42}5  {c |}{col 46}{res}{space 2} .2072925{col 58}{space 2} .1087978{col 69}{space 1}    1.91{col 78}{space 3}0.060{col 86}{space 4}-.0084577{col 99}{space 3} .4230427
{txt}{space 42}6  {c |}{col 46}{res}{space 2} .1788376{col 58}{space 2} .1162236{col 69}{space 1}    1.54{col 78}{space 3}0.127{col 86}{space 4}-.0516381{col 99}{space 3} .4093133
{txt}{space 42}7  {c |}{col 46}{res}{space 2} .3237542{col 58}{space 2} .1798379{col 69}{space 1}    1.80{col 78}{space 3}0.075{col 86}{space 4}-.0328711{col 99}{space 3} .6803795
{txt}{space 42}8  {c |}{col 46}{res}{space 2} .2704471{col 58}{space 2} .1458921{col 69}{space 1}    1.85{col 78}{space 3}0.067{col 86}{space 4}-.0188625{col 99}{space 3} .5597566
{txt}{space 42}9  {c |}{col 46}{res}{space 2}-.0443565{col 58}{space 2} .0210305{col 69}{space 1}   -2.11{col 78}{space 3}0.037{col 86}{space 4}-.0860606{col 99}{space 3}-.0026523
{txt}{space 41}10  {c |}{col 46}{res}{space 2} .2567173{col 58}{space 2}  .195774{col 69}{space 1}    1.31{col 78}{space 3}0.193{col 86}{space 4}-.1315099{col 99}{space 3} .6449445
{txt}{space 41}11  {c |}{col 46}{res}{space 2} .1973867{col 58}{space 2} .0867232{col 69}{space 1}    2.28{col 78}{space 3}0.025{col 86}{space 4} .0254114{col 99}{space 3}  .369362
{txt}{space 41}12  {c |}{col 46}{res}{space 2} .3316132{col 58}{space 2} .1960816{col 69}{space 1}    1.69{col 78}{space 3}0.094{col 86}{space 4} -.057224{col 99}{space 3} .7204505
{txt}{space 41}13  {c |}{col 46}{res}{space 2} .3213281{col 58}{space 2} .1580232{col 69}{space 1}    2.03{col 78}{space 3}0.045{col 86}{space 4} .0079621{col 99}{space 3} .6346941
{txt}{space 41}14  {c |}{col 46}{res}{space 2} .1469329{col 58}{space 2} .1110514{col 69}{space 1}    1.32{col 78}{space 3}0.189{col 86}{space 4}-.0732861{col 99}{space 3}  .367152
{txt}{space 41}15  {c |}{col 46}{res}{space 2} .3137909{col 58}{space 2} .1213617{col 69}{space 1}    2.59{col 78}{space 3}0.011{col 86}{space 4}  .073126{col 99}{space 3} .5544557
{txt}{space 41}16  {c |}{col 46}{res}{space 2} .3903805{col 58}{space 2} .1940655{col 69}{space 1}    2.01{col 78}{space 3}0.047{col 86}{space 4} .0055412{col 99}{space 3} .7752198
{txt}{space 41}17  {c |}{col 46}{res}{space 2} .1134127{col 58}{space 2} .1547261{col 69}{space 1}    0.73{col 78}{space 3}0.465{col 86}{space 4}-.1934151{col 99}{space 3} .4202404
{txt}{space 41}18  {c |}{col 46}{res}{space 2} .3511983{col 58}{space 2} .1542616{col 69}{space 1}    2.28{col 78}{space 3}0.025{col 86}{space 4} .0452917{col 99}{space 3} .6571048
{txt}{space 41}19  {c |}{col 46}{res}{space 2} .2035582{col 58}{space 2} .1049419{col 69}{space 1}    1.94{col 78}{space 3}0.055{col 86}{space 4}-.0045456{col 99}{space 3}  .411662
{txt}{space 41}20  {c |}{col 46}{res}{space 2} .0877369{col 58}{space 2} .1112709{col 69}{space 1}    0.79{col 78}{space 3}0.432{col 86}{space 4}-.1329176{col 99}{space 3} .3083913
{txt}{space 41}21  {c |}{col 46}{res}{space 2} .2374591{col 58}{space 2} .0977192{col 69}{space 1}    2.43{col 78}{space 3}0.017{col 86}{space 4} .0436783{col 99}{space 3} .4312398
{txt}{space 41}22  {c |}{col 46}{res}{space 2} .2116565{col 58}{space 2} .1457191{col 69}{space 1}    1.45{col 78}{space 3}0.149{col 86}{space 4}-.0773099{col 99}{space 3} .5006228
{txt}{space 41}23  {c |}{col 46}{res}{space 2}-.0481245{col 58}{space 2} .0614555{col 69}{space 1}   -0.78{col 78}{space 3}0.435{col 86}{space 4}-.1699931{col 99}{space 3}  .073744
{txt}{space 41}24  {c |}{col 46}{res}{space 2} .2655567{col 58}{space 2} .1118378{col 69}{space 1}    2.37{col 78}{space 3}0.019{col 86}{space 4} .0437781{col 99}{space 3} .4873353
{txt}{space 41}25  {c |}{col 46}{res}{space 2}  .232033{col 58}{space 2} .1252424{col 69}{space 1}    1.85{col 78}{space 3}0.067{col 86}{space 4}-.0163274{col 99}{space 3} .4803935
{txt}{space 41}26  {c |}{col 46}{res}{space 2} .0790562{col 58}{space 2} .1137382{col 69}{space 1}    0.70{col 78}{space 3}0.489{col 86}{space 4}-.1464908{col 99}{space 3} .3046033
{txt}{space 41}27  {c |}{col 46}{res}{space 2} .3557121{col 58}{space 2}  .186543{col 69}{space 1}    1.91{col 78}{space 3}0.059{col 86}{space 4}-.0142097{col 99}{space 3}  .725634
{txt}{space 41}28  {c |}{col 46}{res}{space 2}-.0068879{col 58}{space 2} .0896031{col 69}{space 1}   -0.08{col 78}{space 3}0.939{col 86}{space 4}-.1845742{col 99}{space 3} .1707984
{txt}{space 41}29  {c |}{col 46}{res}{space 2} .1197546{col 58}{space 2} .1630354{col 69}{space 1}    0.73{col 78}{space 3}0.464{col 86}{space 4}-.2035507{col 99}{space 3} .4430599
{txt}{space 41}30  {c |}{col 46}{res}{space 2} .1790198{col 58}{space 2} .1539686{col 69}{space 1}    1.16{col 78}{space 3}0.248{col 86}{space 4}-.1263058{col 99}{space 3} .4843454
{txt}{space 41}31  {c |}{col 46}{res}{space 2} .0290733{col 58}{space 2} .1586082{col 69}{space 1}    0.18{col 78}{space 3}0.855{col 86}{space 4}-.2854526{col 99}{space 3} .3435992
{txt}{space 41}32  {c |}{col 46}{res}{space 2} .2421171{col 58}{space 2} .1252539{col 69}{space 1}    1.93{col 78}{space 3}0.056{col 86}{space 4}-.0062661{col 99}{space 3} .4905003
{txt}{space 41}33  {c |}{col 46}{res}{space 2} .1571623{col 58}{space 2} .0717145{col 69}{space 1}    2.19{col 78}{space 3}0.031{col 86}{space 4} .0149497{col 99}{space 3}  .299375
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{txt}{space 44} {c |}
{space 40}year {c |}
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{txt}{space 44} {c |}
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{txt}{hline 45}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16} 2,507,103{col 28} -1943049{col 39} -1891489{col 50}    20{col 58}  3783018{col 69}  3783272
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] IC note}}.{p_end}

{com}. *
. 
. ** BY NON-SUPERVISORS RESPONDENT: WITHIN-IDENTITY "OUT" GROUP STATUS DIFFERENTIAL BETWEEN MINORITY/NON-MINORITY RESPONDENTS **
. 
. lincom c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0350634{col 26}{space 2} .0368458{col 37}{space 1}    0.95{col 46}{space 3}0.343{col 54}{space 4}-.0380032{col 67}{space 3} .1081301
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  1.minority#c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.minority#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0421782{col 26}{space 2} .0188612{col 37}{space 1}    2.24{col 46}{space 3}0.027{col 54}{space 4} .0047757{col 67}{space 3} .0795807
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *
. *
. *
. *
. 
. ** BY SUPERVISOR RESPONDENT: WITHIN-IDENTITY "OUT" GROUP STATUS DIFFERENTIAL BETWEEN MINORITY/NON-MINORITY RESPONDENTS **
. 
. lincom c.ln_ratio_mnmsup_mnmsub + 1.supervisor#c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}ln_ratio_mnmsup_mnmsub + 1.supervisor#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0451156{col 26}{space 2} .0408936{col 37}{space 1}    1.10{col 46}{space 3}0.272{col 54}{space 4}-.0359779{col 67}{space 3} .1262092
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  1.minority#c.ln_ratio_mnmsup_mnmsub +  1.minority#1.supervisor#c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.minority#c.ln_ratio_mnmsup_mnmsub + 1.minority#1.supervisor#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0694948{col 26}{space 2} .0162739{col 37}{space 1}    4.27{col 46}{space 3}0.000{col 54}{space 4} .0372231{col 67}{space 3} .1017665
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. * SUPERVISOR - NON-SUPERVISORY DIFFERENCE AMONG MINORITY RESPONDENT DIFFERENCES 
.  
. lincom  1.minority#c.ln_ratio_mnmsup_mnmsub +  1.minority#1.supervisor#c.ln_ratio_mnmsup_mnmsub - (1.minority#c.ln_ratio_mnmsup_mnmsub)

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.minority#1.supervisor#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0273166{col 26}{space 2} .0174419{col 37}{space 1}    1.57{col 46}{space 3}0.120{col 54}{space 4}-.0072713{col 67}{space 3} .0619045
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. ******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
.    
. *** MODEL G7.1: CONDITIONAL RESPONSES BY GENDER & POSITION -- GENDER BETWEEN-IDENTITY GROUP STATUS DIFFERENTIAL MODEL: [WOMEN SUPERVISORS WITHIN AGENCY j IN YEAR t / MEN SUPERVISORS WITHIN AGENCY j IN YEAR t] / [WOMEN NON-SUPERVISORS WITHIN AGENCY j IN YEAR t / MEN NON-SUPERVISORS WITHIN AGENCY j IN YEAR t] -- CONTROLLING FOR GENDER SUPERVISORY EMPLOYEE IDENTITY GROUP DIFFERENTIAL ***
. 
. regress lndiversity2zeroadj  c.ln_ratio_fmsup_fmsub##i.women_het##i.supervisor      minority   topoffgender_2 lntotworkforce_count  ln_professionals_total_ratio   i.agencyid i.year, vce(cluster agencyid)

{txt}Linear regression                               Number of obs     = {res} 2,507,103
                                                {txt}{help j_robustsingular:F(23, 104) }       =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0407
                                                {txt}Root MSE          =    {res} .51443

{txt}{ralign 109:(Std. err. adjusted for {res:105} clusters in {res:agencyid})}
{hline 44}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 45}{c |}{col 57}    Robust
{col 1}                        lndiversity2zeroadj{col 45}{c |} Coefficient{col 57}  std. err.{col 69}      t{col 77}   P>|t|{col 85}     [95% con{col 98}f. interval]
{hline 44}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}ln_ratio_fmsup_fmsub {c |}{col 45}{res}{space 2} .1362593{col 57}{space 2} .0452402{col 68}{space 1}    3.01{col 77}{space 3}0.003{col 85}{space 4} .0465462{col 98}{space 3} .2259723
{txt}{space 43} {c |}
{space 34}women_het {c |}
{space 41}1  {c |}{col 45}{res}{space 2}  -.02707{col 57}{space 2} .0111816{col 68}{space 1}   -2.42{col 77}{space 3}0.017{col 85}{space 4}-.0492434{col 98}{space 3}-.0048966
{txt}{space 41}2  {c |}{col 45}{res}{space 2}-.0576914{col 57}{space 2} .0105102{col 68}{space 1}   -5.49{col 77}{space 3}0.000{col 85}{space 4}-.0785335{col 98}{space 3}-.0368494
{txt}{space 43} {c |}
{space 11}women_het#c.ln_ratio_fmsup_fmsub {c |}
{space 41}1  {c |}{col 45}{res}{space 2} .0011383{col 57}{space 2} .0281564{col 68}{space 1}    0.04{col 77}{space 3}0.968{col 85}{space 4} -.054697{col 98}{space 3} .0569736
{txt}{space 41}2  {c |}{col 45}{res}{space 2} .0241413{col 57}{space 2}  .028388{col 68}{space 1}    0.85{col 77}{space 3}0.397{col 85}{space 4}-.0321531{col 98}{space 3} .0804357
{txt}{space 43} {c |}
{space 31}1.supervisor {c |}{col 45}{res}{space 2} .1458792{col 57}{space 2}  .020983{col 68}{space 1}    6.95{col 77}{space 3}0.000{col 85}{space 4} .1042691{col 98}{space 3} .1874893
{txt}{space 43} {c |}
{space 10}supervisor#c.ln_ratio_fmsup_fmsub {c |}
{space 41}1  {c |}{col 45}{res}{space 2} .0577171{col 57}{space 2} .0419002{col 68}{space 1}    1.38{col 77}{space 3}0.171{col 85}{space 4}-.0253726{col 98}{space 3} .1408068
{txt}{space 43} {c |}
{space 23}women_het#supervisor {c |}
{space 39}1 1  {c |}{col 45}{res}{space 2} .0040224{col 57}{space 2} .0134315{col 68}{space 1}    0.30{col 77}{space 3}0.765{col 85}{space 4}-.0226127{col 98}{space 3} .0306576
{txt}{space 39}2 1  {c |}{col 45}{res}{space 2} .0023104{col 57}{space 2}   .01973{col 68}{space 1}    0.12{col 77}{space 3}0.907{col 85}{space 4}-.0368148{col 98}{space 3} .0414357
{txt}{space 43} {c |}
women_het#supervisor#c.ln_ratio_fmsup_fmsub {c |}
{space 39}1 1  {c |}{col 45}{res}{space 2} .0064572{col 57}{space 2} .0293147{col 68}{space 1}    0.22{col 77}{space 3}0.826{col 85}{space 4} -.051675{col 98}{space 3} .0645893
{txt}{space 39}2 1  {c |}{col 45}{res}{space 2}-.0274419{col 57}{space 2} .0395705{col 68}{space 1}   -0.69{col 77}{space 3}0.490{col 85}{space 4}-.1059116{col 98}{space 3} .0510279
{txt}{space 43} {c |}
{space 35}minority {c |}{col 45}{res}{space 2}-.0768903{col 57}{space 2} .0036452{col 68}{space 1}  -21.09{col 77}{space 3}0.000{col 85}{space 4} -.084119{col 98}{space 3}-.0696617
{txt}{space 29}topoffgender_2 {c |}{col 45}{res}{space 2} -.002672{col 57}{space 2} .0047385{col 68}{space 1}   -0.56{col 77}{space 3}0.574{col 85}{space 4}-.0120686{col 98}{space 3} .0067246
{txt}{space 23}lntotworkforce_count {c |}{col 45}{res}{space 2} .0758904{col 57}{space 2} .0321201{col 68}{space 1}    2.36{col 77}{space 3}0.020{col 85}{space 4}  .012195{col 98}{space 3} .1395857
{txt}{space 15}ln_professionals_total_ratio {c |}{col 45}{res}{space 2} .0071597{col 57}{space 2} .0411777{col 68}{space 1}    0.17{col 77}{space 3}0.862{col 85}{space 4}-.0744971{col 98}{space 3} .0888166
{txt}{space 43} {c |}
{space 35}agencyid {c |}
{space 41}2  {c |}{col 45}{res}{space 2} .3094772{col 57}{space 2} .1067001{col 68}{space 1}    2.90{col 77}{space 3}0.005{col 85}{space 4} .0978868{col 98}{space 3} .5210675
{txt}{space 41}3  {c |}{col 45}{res}{space 2} .0911296{col 57}{space 2} .0492037{col 68}{space 1}    1.85{col 77}{space 3}0.067{col 85}{space 4}-.0064431{col 98}{space 3} .1887023
{txt}{space 41}4  {c |}{col 45}{res}{space 2} .4314853{col 57}{space 2} .1180059{col 68}{space 1}    3.66{col 77}{space 3}0.000{col 85}{space 4} .1974753{col 98}{space 3} .6654954
{txt}{space 41}5  {c |}{col 45}{res}{space 2} .2611908{col 57}{space 2} .0902899{col 68}{space 1}    2.89{col 77}{space 3}0.005{col 85}{space 4} .0821425{col 98}{space 3} .4402391
{txt}{space 41}6  {c |}{col 45}{res}{space 2} .2496878{col 57}{space 2} .1098895{col 68}{space 1}    2.27{col 77}{space 3}0.025{col 85}{space 4} .0317727{col 98}{space 3} .4676028
{txt}{space 41}7  {c |}{col 45}{res}{space 2} .3512245{col 57}{space 2} .1523507{col 68}{space 1}    2.31{col 77}{space 3}0.023{col 85}{space 4} .0491073{col 98}{space 3} .6533417
{txt}{space 41}8  {c |}{col 45}{res}{space 2} .2986636{col 57}{space 2} .1258326{col 68}{space 1}    2.37{col 77}{space 3}0.019{col 85}{space 4} .0491328{col 98}{space 3} .5481943
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{txt}{space 40}70  {c |}{col 45}{res}{space 2} .4042462{col 57}{space 2} .1426584{col 68}{space 1}    2.83{col 77}{space 3}0.006{col 85}{space 4} .1213493{col 98}{space 3} .6871432
{txt}{space 40}71  {c |}{col 45}{res}{space 2} .1108265{col 57}{space 2} .0881936{col 68}{space 1}    1.26{col 77}{space 3}0.212{col 85}{space 4}-.0640648{col 98}{space 3} .2857178
{txt}{space 40}72  {c |}{col 45}{res}{space 2} .2699077{col 57}{space 2} .0926536{col 68}{space 1}    2.91{col 77}{space 3}0.004{col 85}{space 4} .0861722{col 98}{space 3} .4536432
{txt}{space 40}73  {c |}{col 45}{res}{space 2} .4898079{col 57}{space 2} .1587709{col 68}{space 1}    3.08{col 77}{space 3}0.003{col 85}{space 4} .1749592{col 98}{space 3} .8046565
{txt}{space 40}74  {c |}{col 45}{res}{space 2} .0918374{col 57}{space 2} .0997694{col 68}{space 1}    0.92{col 77}{space 3}0.359{col 85}{space 4} -.106009{col 98}{space 3} .2896838
{txt}{space 40}75  {c |}{col 45}{res}{space 2} .1869837{col 57}{space 2} .0933935{col 68}{space 1}    2.00{col 77}{space 3}0.048{col 85}{space 4} .0017808{col 98}{space 3} .3721865
{txt}{space 40}76  {c |}{col 45}{res}{space 2} .3370266{col 57}{space 2} .1524503{col 68}{space 1}    2.21{col 77}{space 3}0.029{col 85}{space 4}  .034712{col 98}{space 3} .6393412
{txt}{space 40}77  {c |}{col 45}{res}{space 2}   .26078{col 57}{space 2}  .126915{col 68}{space 1}    2.05{col 77}{space 3}0.042{col 85}{space 4} .0091027{col 98}{space 3} .5124573
{txt}{space 40}78  {c |}{col 45}{res}{space 2} .2965367{col 57}{space 2} .0988826{col 68}{space 1}    3.00{col 77}{space 3}0.003{col 85}{space 4} .1004489{col 98}{space 3} .4926246
{txt}{space 40}79  {c |}{col 45}{res}{space 2}-.0136251{col 57}{space 2} .0158682{col 68}{space 1}   -0.86{col 77}{space 3}0.393{col 85}{space 4}-.0450923{col 98}{space 3} .0178421
{txt}{space 40}80  {c |}{col 45}{res}{space 2} .4324777{col 57}{space 2} .1591391{col 68}{space 1}    2.72{col 77}{space 3}0.008{col 85}{space 4} .1168989{col 98}{space 3} .7480565
{txt}{space 40}81  {c |}{col 45}{res}{space 2} .2131287{col 57}{space 2} .1691991{col 68}{space 1}    1.26{col 77}{space 3}0.211{col 85}{space 4}-.1223995{col 98}{space 3} .5486568
{txt}{space 40}82  {c |}{col 45}{res}{space 2} .3512594{col 57}{space 2} .1626891{col 68}{space 1}    2.16{col 77}{space 3}0.033{col 85}{space 4} .0286408{col 98}{space 3}  .673878
{txt}{space 40}83  {c |}{col 45}{res}{space 2} .4293559{col 57}{space 2} .1426056{col 68}{space 1}    3.01{col 77}{space 3}0.003{col 85}{space 4} .1465637{col 98}{space 3} .7121482
{txt}{space 40}84  {c |}{col 45}{res}{space 2} .3278789{col 57}{space 2} .1664475{col 68}{space 1}    1.97{col 77}{space 3}0.052{col 85}{space 4}-.0021928{col 98}{space 3} .6579506
{txt}{space 40}85  {c |}{col 45}{res}{space 2} .3992819{col 57}{space 2}  .111229{col 68}{space 1}    3.59{col 77}{space 3}0.001{col 85}{space 4} .1787106{col 98}{space 3} .6198533
{txt}{space 40}86  {c |}{col 45}{res}{space 2} .4616813{col 57}{space 2} .1755422{col 68}{space 1}    2.63{col 77}{space 3}0.010{col 85}{space 4} .1135746{col 98}{space 3}  .809788
{txt}{space 40}87  {c |}{col 45}{res}{space 2} .4635382{col 57}{space 2} .1629675{col 68}{space 1}    2.84{col 77}{space 3}0.005{col 85}{space 4} .1403676{col 98}{space 3} .7867088
{txt}{space 40}88  {c |}{col 45}{res}{space 2} .2511638{col 57}{space 2} .1137534{col 68}{space 1}    2.21{col 77}{space 3}0.029{col 85}{space 4} .0255866{col 98}{space 3}  .476741
{txt}{space 40}89  {c |}{col 45}{res}{space 2} .2977907{col 57}{space 2} .1399803{col 68}{space 1}    2.13{col 77}{space 3}0.036{col 85}{space 4} .0202045{col 98}{space 3} .5753768
{txt}{space 40}90  {c |}{col 45}{res}{space 2} .0700286{col 57}{space 2} .0416642{col 68}{space 1}    1.68{col 77}{space 3}0.096{col 85}{space 4} -.012593{col 98}{space 3} .1526503
{txt}{space 40}91  {c |}{col 45}{res}{space 2} .1900332{col 57}{space 2} .0902778{col 68}{space 1}    2.10{col 77}{space 3}0.038{col 85}{space 4} .0110089{col 98}{space 3} .3690574
{txt}{space 40}92  {c |}{col 45}{res}{space 2} .0819261{col 57}{space 2} .0454889{col 68}{space 1}    1.80{col 77}{space 3}0.075{col 85}{space 4}-.0082801{col 98}{space 3} .1721322
{txt}{space 40}93  {c |}{col 45}{res}{space 2} .4297076{col 57}{space 2} .1435693{col 68}{space 1}    2.99{col 77}{space 3}0.003{col 85}{space 4} .1450043{col 98}{space 3} .7144109
{txt}{space 40}94  {c |}{col 45}{res}{space 2} .4689299{col 57}{space 2} .1660096{col 68}{space 1}    2.82{col 77}{space 3}0.006{col 85}{space 4} .1397267{col 98}{space 3} .7981331
{txt}{space 40}95  {c |}{col 45}{res}{space 2} .2212146{col 57}{space 2} .1445694{col 68}{space 1}    1.53{col 77}{space 3}0.129{col 85}{space 4}-.0654719{col 98}{space 3} .5079011
{txt}{space 40}96  {c |}{col 45}{res}{space 2} .3398339{col 57}{space 2} .1436871{col 68}{space 1}    2.37{col 77}{space 3}0.020{col 85}{space 4}  .054897{col 98}{space 3} .6247708
{txt}{space 40}97  {c |}{col 45}{res}{space 2} .3911949{col 57}{space 2}  .125022{col 68}{space 1}    3.13{col 77}{space 3}0.002{col 85}{space 4} .1432715{col 98}{space 3} .6391183
{txt}{space 40}98  {c |}{col 45}{res}{space 2} .5595926{col 57}{space 2} .1772392{col 68}{space 1}    3.16{col 77}{space 3}0.002{col 85}{space 4} .2081205{col 98}{space 3} .9110646
{txt}{space 40}99  {c |}{col 45}{res}{space 2} .0818552{col 57}{space 2} .0245634{col 68}{space 1}    3.33{col 77}{space 3}0.001{col 85}{space 4} .0331451{col 98}{space 3} .1305654
{txt}{space 39}100  {c |}{col 45}{res}{space 2} .2544796{col 57}{space 2} .1464565{col 68}{space 1}    1.74{col 77}{space 3}0.085{col 85}{space 4}-.0359491{col 98}{space 3} .5449084
{txt}{space 39}101  {c |}{col 45}{res}{space 2} .4029352{col 57}{space 2} .1190357{col 68}{space 1}    3.38{col 77}{space 3}0.001{col 85}{space 4} .1668829{col 98}{space 3} .6389875
{txt}{space 39}102  {c |}{col 45}{res}{space 2} .2546501{col 57}{space 2} .1544651{col 68}{space 1}    1.65{col 77}{space 3}0.102{col 85}{space 4}  -.05166{col 98}{space 3} .5609602
{txt}{space 39}103  {c |}{col 45}{res}{space 2} .0685888{col 57}{space 2} .0771303{col 68}{space 1}    0.89{col 77}{space 3}0.376{col 85}{space 4}-.0843635{col 98}{space 3} .2215412
{txt}{space 39}104  {c |}{col 45}{res}{space 2}-.1089871{col 57}{space 2} .0394843{col 68}{space 1}   -2.76{col 77}{space 3}0.007{col 85}{space 4} -.187286{col 98}{space 3}-.0306883
{txt}{space 39}105  {c |}{col 45}{res}{space 2}  .334245{col 57}{space 2}  .142269{col 68}{space 1}    2.35{col 77}{space 3}0.021{col 85}{space 4} .0521202{col 98}{space 3} .6163697
{txt}{space 43} {c |}
{space 39}year {c |}
{space 38}2011  {c |}{col 45}{res}{space 2}-.0040744{col 57}{space 2} .0032983{col 68}{space 1}   -1.24{col 77}{space 3}0.220{col 85}{space 4}-.0106151{col 98}{space 3} .0024663
{txt}{space 38}2012  {c |}{col 45}{res}{space 2} -.000225{col 57}{space 2} .0045134{col 68}{space 1}   -0.05{col 77}{space 3}0.960{col 85}{space 4}-.0091754{col 98}{space 3} .0087253
{txt}{space 38}2013  {c |}{col 45}{res}{space 2}-.0012263{col 57}{space 2} .0047793{col 68}{space 1}   -0.26{col 77}{space 3}0.798{col 85}{space 4}-.0107039{col 98}{space 3} .0082513
{txt}{space 38}2014  {c |}{col 45}{res}{space 2}-.0072112{col 57}{space 2} .0065078{col 68}{space 1}   -1.11{col 77}{space 3}0.270{col 85}{space 4}-.0201164{col 98}{space 3}  .005694
{txt}{space 38}2015  {c |}{col 45}{res}{space 2}-.0147481{col 57}{space 2} .0079423{col 68}{space 1}   -1.86{col 77}{space 3}0.066{col 85}{space 4} -.030498{col 98}{space 3} .0010019
{txt}{space 38}2016  {c |}{col 45}{res}{space 2} -.018073{col 57}{space 2} .0068236{col 68}{space 1}   -2.65{col 77}{space 3}0.009{col 85}{space 4}-.0316045{col 98}{space 3}-.0045416
{txt}{space 38}2017  {c |}{col 45}{res}{space 2}-.0037117{col 57}{space 2} .0091672{col 68}{space 1}   -0.40{col 77}{space 3}0.686{col 85}{space 4}-.0218906{col 98}{space 3} .0144672
{txt}{space 38}2018  {c |}{col 45}{res}{space 2}-.0328449{col 57}{space 2} .0067458{col 68}{space 1}   -4.87{col 77}{space 3}0.000{col 85}{space 4} -.046222{col 98}{space 3}-.0194677
{txt}{space 38}2019  {c |}{col 45}{res}{space 2}-.0711692{col 57}{space 2} .0081078{col 68}{space 1}   -8.78{col 77}{space 3}0.000{col 85}{space 4}-.0872472{col 98}{space 3}-.0550912
{txt}{space 43} {c |}
{space 38}_cons {c |}{col 45}{res}{space 2}-.1379502{col 57}{space 2}  .407729{col 68}{space 1}   -0.34{col 77}{space 3}0.736{col 85}{space 4}-.9464921{col 98}{space 3} .6705916
{txt}{hline 44}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16} 2,507,103{col 28} -1943049{col 39} -1890910{col 50}    24{col 58}  3781868{col 69}  3782174
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] IC note}}.{p_end}

{com}. *
. ** BY NON-SUPERVISORS RESPONDENT: BETWEEN-IDENTITY "IN" GROUP VERSUS "OUT" GROUP STATUS DIFFERENTIAL BETWEEN GENDERED RESPONDENTS **
. 
. lincom c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .1362593{col 26}{space 2} .0452402{col 37}{space 1}    3.01{col 46}{space 3}0.003{col 54}{space 4} .0465462{col 67}{space 3} .2259723
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  1.women_het#c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.women_het#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0011383{col 26}{space 2} .0281564{col 37}{space 1}    0.04{col 46}{space 3}0.968{col 54}{space 4} -.054697{col 67}{space 3} .0569736
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  2.women_het#c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}2.women_het#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0241413{col 26}{space 2}  .028388{col 37}{space 1}    0.85{col 46}{space 3}0.397{col 54}{space 4}-.0321531{col 67}{space 3} .0804357
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. lincom 2.women_het#c.ln_ratio_fmsup_fmsub -  1.women_het#c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1}{space 1}{res}- 1.women_het#c.ln_ratio_fmsup_fmsub + 2.women_het#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}  .023003{col 26}{space 2}  .019276{col 37}{space 1}    1.19{col 46}{space 3}0.235{col 54}{space 4}-.0152221{col 67}{space 3} .0612281
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. *
. 
. 
. ** BY SUPERVISOR RESPONDENT:BETWEEN-IDENTITY "IN" GROUP VERSUS "OUT" GROUP STATUS DIFFERENTIAL BETWEENGENDERED RESPONDENTS **
. 
. lincom c.ln_ratio_fmsup_fmsub + 1.supervisor#c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}ln_ratio_fmsup_fmsub + 1.supervisor#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .1939763{col 26}{space 2} .0487098{col 37}{space 1}    3.98{col 46}{space 3}0.000{col 54}{space 4} .0973829{col 67}{space 3} .2905698
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  1.women_het#c.ln_ratio_fmsup_fmsub +  1.women_het#1.supervisor#c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.women_het#c.ln_ratio_fmsup_fmsub + 1.women_het#1.supervisor#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0075955{col 26}{space 2} .0157419{col 37}{space 1}    0.48{col 46}{space 3}0.630{col 54}{space 4}-.0236213{col 67}{space 3} .0388122
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  2.women_het#c.ln_ratio_fmsup_fmsub +  2.women_het#1.supervisor#c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}2.women_het#c.ln_ratio_fmsup_fmsub + 2.women_het#1.supervisor#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.0033006{col 26}{space 2} .0340863{col 37}{space 1}   -0.10{col 46}{space 3}0.923{col 54}{space 4}-.0708949{col 67}{space 3} .0642938
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. lincom  2.women_het#c.ln_ratio_fmsup_fmsub +  2.women_het#1.supervisor#c.ln_ratio_fmsup_fmsub - (1.women_het#c.ln_ratio_fmsup_fmsub +  1.women_het#1.supervisor#c.ln_ratio_fmsup_fmsub)

{p 0 7}{space 1}{text:( 1)}{space 1}{space 1}{res}- 1.women_het#c.ln_ratio_fmsup_fmsub + 2.women_het#c.ln_ratio_fmsup_fmsub - 1.women_het#1.supervisor#c.ln_ratio_fmsup_fmsub + 2.women_het#1.supervisor#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} -.010896{col 26}{space 2} .0303363{col 37}{space 1}   -0.36{col 46}{space 3}0.720{col 54}{space 4}-.0710541{col 67}{space 3}  .049262
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. * SUPERVISOR - NON-SUPERVISORY DIFFERENCE AMONG WOMEN RESPONDENT DIFFERENCES [NON-MINORITY WOMEN RESPONDENTS FOLLOWED BY MINORITY WOMEN RESPONDENTS] -- DO NOT PLOT IN GRAPHS [ONLY FOR TEXT]!
. 
. lincom  1.women_het#c.ln_ratio_fmsup_fmsub +  1.women_het#1.supervisor#c.ln_ratio_fmsup_fmsub  - (1.women_het#c.ln_ratio_fmsup_fmsub)

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.women_het#1.supervisor#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0064572{col 26}{space 2} .0293147{col 37}{space 1}    0.22{col 46}{space 3}0.826{col 54}{space 4} -.051675{col 67}{space 3} .0645893
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. lincom  2.women_het#c.ln_ratio_fmsup_fmsub +  2.women_het#1.supervisor#c.ln_ratio_fmsup_fmsub  - (2.women_het#c.ln_ratio_fmsup_fmsub)

{p 0 7}{space 1}{text:( 1)}{space 1} {res}2.women_het#1.supervisor#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.0274419{col 26}{space 2} .0395705{col 37}{space 1}   -0.69{col 46}{space 3}0.490{col 54}{space 4}-.1059116{col 67}{space 3} .0510279
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. 
. 
. 
.    
. *** MODEL G8.1: CONDITIONAL RESPONSES BY RACE/ETHNICITY & POSITION -- RACIAL/ETHNIC BETWEEN-IDENTITY GROUP STATUS DIFFERENTIAL MODEL: [MINORITY SUPERVISORS WITHIN AGENCY j IN YEAR t / NON-MINORITY SUPERVISORS WITHIN AGENCY j IN YEAR t] / [MINORITY NON-SUPERVISORS WITHIN AGENCY j IN YEAR t / NON-MINORITY NON-SUPERVISORS WITHIN AGENCY j IN YEAR t] -- CONTROLLING FOR RACIAL/ETHNIC SUPERVISORY EMPLOYEE IDENTITY GROUP DIFFERENTIAL  ***
. 
. regress  lndiversity2zeroadj  c.ln_ratio_mnmsup_mnmsub##i.minority_het##i.supervisor     gender  topoffminority_2 lntotworkforce_count  ln_professionals_total_ratio   i.agencyid i.year if e(sample), vce(cluster agencyid)

{txt}Linear regression                               Number of obs     = {res} 2,507,103
                                                {txt}{help j_robustsingular:F(23, 104) }       =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0406
                                                {txt}Root MSE          =    {res} .51448

{txt}{ralign 114:(Std. err. adjusted for {res:105} clusters in {res:agencyid})}
{hline 49}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 50}{c |}{col 62}    Robust
{col 1}                             lndiversity2zeroadj{col 50}{c |} Coefficient{col 62}  std. err.{col 74}      t{col 82}   P>|t|{col 90}     [95% con{col 103}f. interval]
{hline 49}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
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{txt}{space 48} {c |}
{space 36}minority_het {c |}
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{txt}{space 48} {c |}
{space 11}minority_het#c.ln_ratio_mnmsup_mnmsub {c |}
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{txt}{space 48} {c |}
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{txt}{space 48} {c |}
{space 13}supervisor#c.ln_ratio_mnmsup_mnmsub {c |}
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{txt}{space 48} {c |}
{space 25}minority_het#supervisor {c |}
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{txt}{space 48} {c |}
minority_het#supervisor#c.ln_ratio_mnmsup_mnmsub {c |}
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{txt}{space 45}83  {c |}{col 50}{res}{space 2} .3785455{col 62}{space 2} .1557207{col 73}{space 1}    2.43{col 82}{space 3}0.017{col 90}{space 4} .0697456{col 103}{space 3} .6873454
{txt}{space 45}84  {c |}{col 50}{res}{space 2} .3434174{col 62}{space 2} .1830939{col 73}{space 1}    1.88{col 82}{space 3}0.064{col 90}{space 4}-.0196647{col 103}{space 3} .7064995
{txt}{space 45}85  {c |}{col 50}{res}{space 2} .3398547{col 62}{space 2}  .139532{col 73}{space 1}    2.44{col 82}{space 3}0.017{col 90}{space 4} .0631575{col 103}{space 3} .6165518
{txt}{space 45}86  {c |}{col 50}{res}{space 2} .4230297{col 62}{space 2} .1981723{col 73}{space 1}    2.13{col 82}{space 3}0.035{col 90}{space 4} .0300466{col 103}{space 3} .8160127
{txt}{space 45}87  {c |}{col 50}{res}{space 2} .4468507{col 62}{space 2}  .207072{col 73}{space 1}    2.16{col 82}{space 3}0.033{col 90}{space 4} .0362192{col 103}{space 3} .8574823
{txt}{space 45}88  {c |}{col 50}{res}{space 2}  .227457{col 62}{space 2} .1396247{col 73}{space 1}    1.63{col 82}{space 3}0.106{col 90}{space 4}-.0494241{col 103}{space 3}  .504338
{txt}{space 45}89  {c |}{col 50}{res}{space 2} .2619961{col 62}{space 2} .1519716{col 73}{space 1}    1.72{col 82}{space 3}0.088{col 90}{space 4}-.0393693{col 103}{space 3} .5633614
{txt}{space 45}90  {c |}{col 50}{res}{space 2} .0866792{col 62}{space 2} .0492331{col 73}{space 1}    1.76{col 82}{space 3}0.081{col 90}{space 4}-.0109519{col 103}{space 3} .1843103
{txt}{space 45}91  {c |}{col 50}{res}{space 2} .1820723{col 62}{space 2} .1004845{col 73}{space 1}    1.81{col 82}{space 3}0.073{col 90}{space 4}-.0171923{col 103}{space 3} .3813369
{txt}{space 45}92  {c |}{col 50}{res}{space 2} .0862583{col 62}{space 2} .0539097{col 73}{space 1}    1.60{col 82}{space 3}0.113{col 90}{space 4}-.0206467{col 103}{space 3} .1931634
{txt}{space 45}93  {c |}{col 50}{res}{space 2} .3871615{col 62}{space 2} .1590099{col 73}{space 1}    2.43{col 82}{space 3}0.017{col 90}{space 4}  .071839{col 103}{space 3}  .702484
{txt}{space 45}94  {c |}{col 50}{res}{space 2} .4611967{col 62}{space 2} .1990744{col 73}{space 1}    2.32{col 82}{space 3}0.022{col 90}{space 4} .0664247{col 103}{space 3} .8559687
{txt}{space 45}95  {c |}{col 50}{res}{space 2} .2467783{col 62}{space 2}  .169685{col 73}{space 1}    1.45{col 82}{space 3}0.149{col 90}{space 4}-.0897135{col 103}{space 3}   .58327
{txt}{space 45}96  {c |}{col 50}{res}{space 2}   .32198{col 62}{space 2} .1729954{col 73}{space 1}    1.86{col 82}{space 3}0.066{col 90}{space 4}-.0210764{col 103}{space 3} .6650364
{txt}{space 45}97  {c |}{col 50}{res}{space 2}  .344647{col 62}{space 2} .1430266{col 73}{space 1}    2.41{col 82}{space 3}0.018{col 90}{space 4} .0610198{col 103}{space 3} .6282742
{txt}{space 45}98  {c |}{col 50}{res}{space 2} .4796799{col 62}{space 2} .2033613{col 73}{space 1}    2.36{col 82}{space 3}0.020{col 90}{space 4} .0764069{col 103}{space 3}  .882953
{txt}{space 45}99  {c |}{col 50}{res}{space 2} .0923281{col 62}{space 2} .0273405{col 73}{space 1}    3.38{col 82}{space 3}0.001{col 90}{space 4} .0381108{col 103}{space 3} .1465454
{txt}{space 44}100  {c |}{col 50}{res}{space 2} .2703397{col 62}{space 2} .1779493{col 73}{space 1}    1.52{col 82}{space 3}0.132{col 90}{space 4}-.0825405{col 103}{space 3} .6232198
{txt}{space 44}101  {c |}{col 50}{res}{space 2} .3972762{col 62}{space 2} .1477023{col 73}{space 1}    2.69{col 82}{space 3}0.008{col 90}{space 4}  .104377{col 103}{space 3} .6901754
{txt}{space 44}102  {c |}{col 50}{res}{space 2} .3168659{col 62}{space 2} .1874185{col 73}{space 1}    1.69{col 82}{space 3}0.094{col 90}{space 4}-.0547919{col 103}{space 3} .6885238
{txt}{space 44}103  {c |}{col 50}{res}{space 2} .1239044{col 62}{space 2} .0942809{col 73}{space 1}    1.31{col 82}{space 3}0.192{col 90}{space 4} -.063058{col 103}{space 3} .3108669
{txt}{space 44}104  {c |}{col 50}{res}{space 2}-.1238941{col 62}{space 2} .0498297{col 73}{space 1}   -2.49{col 82}{space 3}0.014{col 90}{space 4}-.2227082{col 103}{space 3}  -.02508
{txt}{space 44}105  {c |}{col 50}{res}{space 2} .2859157{col 62}{space 2} .1702326{col 73}{space 1}    1.68{col 82}{space 3}0.096{col 90}{space 4}-.0516619{col 103}{space 3} .6234933
{txt}{space 48} {c |}
{space 44}year {c |}
{space 43}2011  {c |}{col 50}{res}{space 2}-.0017842{col 62}{space 2} .0039474{col 73}{space 1}   -0.45{col 82}{space 3}0.652{col 90}{space 4}-.0096121{col 103}{space 3} .0060437
{txt}{space 43}2012  {c |}{col 50}{res}{space 2} .0064257{col 62}{space 2} .0048964{col 73}{space 1}    1.31{col 82}{space 3}0.192{col 90}{space 4} -.003284{col 103}{space 3} .0161354
{txt}{space 43}2013  {c |}{col 50}{res}{space 2}  .005592{col 62}{space 2} .0057745{col 73}{space 1}    0.97{col 82}{space 3}0.335{col 90}{space 4} -.005859{col 103}{space 3}  .017043
{txt}{space 43}2014  {c |}{col 50}{res}{space 2} .0017531{col 62}{space 2} .0062866{col 73}{space 1}    0.28{col 82}{space 3}0.781{col 90}{space 4}-.0107135{col 103}{space 3} .0142198
{txt}{space 43}2015  {c |}{col 50}{res}{space 2}-.0047951{col 62}{space 2} .0071708{col 73}{space 1}   -0.67{col 82}{space 3}0.505{col 90}{space 4}-.0190151{col 103}{space 3} .0094249
{txt}{space 43}2016  {c |}{col 50}{res}{space 2}-.0063697{col 62}{space 2}  .006372{col 73}{space 1}   -1.00{col 82}{space 3}0.320{col 90}{space 4}-.0190056{col 103}{space 3} .0062661
{txt}{space 43}2017  {c |}{col 50}{res}{space 2} .0056163{col 62}{space 2} .0098101{col 73}{space 1}    0.57{col 82}{space 3}0.568{col 90}{space 4}-.0138375{col 103}{space 3} .0250701
{txt}{space 43}2018  {c |}{col 50}{res}{space 2}-.0197512{col 62}{space 2} .0069558{col 73}{space 1}   -2.84{col 82}{space 3}0.005{col 90}{space 4}-.0335448{col 103}{space 3}-.0059576
{txt}{space 43}2019  {c |}{col 50}{res}{space 2}-.0578134{col 62}{space 2}  .007755{col 73}{space 1}   -7.46{col 82}{space 3}0.000{col 90}{space 4}-.0731918{col 103}{space 3} -.042435
{txt}{space 48} {c |}
{space 43}_cons {c |}{col 50}{res}{space 2}-.0813607{col 62}{space 2} .4528817{col 73}{space 1}   -0.18{col 82}{space 3}0.858{col 90}{space 4}-.9794421{col 103}{space 3} .8167207
{txt}{hline 49}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16} 2,507,103{col 28} -1943049{col 39} -1891128{col 50}    24{col 58}  3782304{col 69}  3782609
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] IC note}}.{p_end}

{com}. *
. 
. ** BY NON-SUPERVISORS RESPONDENT: BETWEEN-IDENTITY "IN" GROUP VERSUS "OUT" GROUP STATUS DIFFERENTIAL BETWEEN MINORITY/NON-MINORITY RESPONDENTS **
. 
. lincom c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0364566{col 26}{space 2} .0368283{col 37}{space 1}    0.99{col 46}{space 3}0.325{col 54}{space 4}-.0365753{col 67}{space 3} .1094884
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  1.minority_het#c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.minority_het#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0148327{col 26}{space 2} .0168284{col 37}{space 1}    0.88{col 46}{space 3}0.380{col 54}{space 4}-.0185387{col 67}{space 3}  .048204
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  2.minority_het#c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}2.minority_het#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0523956{col 26}{space 2} .0216843{col 37}{space 1}    2.42{col 46}{space 3}0.017{col 54}{space 4} .0093948{col 67}{space 3} .0953964
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. lincom  2.minority_het#c.ln_ratio_mnmsup_mnmsub - 1.minority_het#c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1}{space 1}{res}- 1.minority_het#c.ln_ratio_mnmsup_mnmsub + 2.minority_het#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0375629{col 26}{space 2} .0173494{col 37}{space 1}    2.17{col 46}{space 3}0.033{col 54}{space 4} .0031585{col 67}{space 3} .0719674
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *
. *
. *
. *
. *
. 
. 
. ** BY SUPERVISOR RESPONDENT: BETWEEN-IDENTITY "IN" GROUP VERSUS "OUT" GROUP STATUS DIFFERENTIAL BETWEEN MINORITY/NON-MINORITY RESPONDENTS **
. 
. lincom c.ln_ratio_mnmsup_mnmsub+ 1.supervisor#c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}ln_ratio_mnmsup_mnmsub + 1.supervisor#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0465351{col 26}{space 2} .0411055{col 37}{space 1}    1.13{col 46}{space 3}0.260{col 54}{space 4}-.0349787{col 67}{space 3} .1280488
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  1.minority_het#c.ln_ratio_mnmsup_mnmsub +  1.minority_het#1.supervisor#c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.minority_het#c.ln_ratio_mnmsup_mnmsub + 1.minority_het#1.supervisor#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0661936{col 26}{space 2}  .020175{col 37}{space 1}    3.28{col 46}{space 3}0.001{col 54}{space 4} .0261858{col 67}{space 3} .1062015
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  2.minority_het#c.ln_ratio_mnmsup_mnmsub +  2.minority_het#1.supervisor#c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}2.minority_het#c.ln_ratio_mnmsup_mnmsub + 2.minority_het#1.supervisor#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0666294{col 26}{space 2} .0231241{col 37}{space 1}    2.88{col 46}{space 3}0.005{col 54}{space 4} .0207734{col 67}{space 3} .1124853
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. lincom  2.minority_het#c.ln_ratio_mnmsup_mnmsub +  2.minority_het#1.supervisor#c.ln_ratio_mnmsup_mnmsub - (1.minority_het#c.ln_ratio_mnmsup_mnmsub +  1.minority_het#1.supervisor#c.ln_ratio_mnmsup_mnmsub)

{p 0 7}{space 1}{text:( 1)}{space 1}{space 1}{res}- 1.minority_het#c.ln_ratio_mnmsup_mnmsub + 2.minority_het#c.ln_ratio_mnmsup_mnmsub - 1.minority_het#1.supervisor#c.ln_ratio_mnmsup_mnmsub + 2.minority_het#1.supervisor#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0004357{col 26}{space 2} .0279747{col 37}{space 1}    0.02{col 46}{space 3}0.988{col 54}{space 4}-.0550391{col 67}{space 3} .0559106
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. *
. 
. 
. * SUPERVISOR - NON-SUPERVISORY DIFFERENCE AMONG MINORITY RESPONDENT DIFFERENCES [MINORITY MEN RESPONDENTS FOLLOWED BY MINORITY WOMEN RESPONDENTS] -- DO NOT PLOT IN GRAPHS [ONLY FOR TEXT]!
. 
. lincom  1.minority_het#c.ln_ratio_mnmsup_mnmsub +  1.minority_het#1.supervisor#c.ln_ratio_mnmsup_mnmsub - (1.minority_het#c.ln_ratio_mnmsup_mnmsub)

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.minority_het#1.supervisor#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}  .051361{col 26}{space 2} .0191977{col 37}{space 1}    2.68{col 46}{space 3}0.009{col 54}{space 4} .0132911{col 67}{space 3} .0894308
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. lincom  2.minority_het#c.ln_ratio_mnmsup_mnmsub +  2.minority_het#1.supervisor#c.ln_ratio_mnmsup_mnmsub - (2.minority_het#c.ln_ratio_mnmsup_mnmsub)

{p 0 7}{space 1}{text:( 1)}{space 1} {res}2.minority_het#1.supervisor#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0142338{col 26}{space 2} .0296888{col 37}{space 1}    0.48{col 46}{space 3}0.633{col 54}{space 4}-.0446402{col 67}{space 3} .0731078
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. *********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
.    
.    
.    
.    
.    
.    
.    
. *** ROBUSTNESS CHECK # 2: EVALUATE SENSITIVITY OF RELATIVE SGPD ESTIMATES WHEN OMITTING 'EXTREME' ABOVE PARITY VALUES OF RELATIVE SGPD MEASURE [ln_ratio_fmsup_fmsub > 0 OR ln_ratio_mnmsup_mnmsub > 0] -- IDEA: RELATIVE SGPD ESTIMATES BASED ON FULL SAMPLE MAY BE BIASED UPWARD IF 'OUT-GROUP' HAS STATUS-GROUP MAJORITY DOMINANCE  ***
. 
. 
. 
. 
. 
. *********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
.  
. 
.    
. *** 4. CONDITIONAL-RESPONDENT MODELS EVALUATING THE RELATIONSHIP INVOLVING WITHIN-IDENTITY "OUT-GROUP" STATUS & BETWEEN-IDENTITY GROUP STATUS DIFFERENTIALS AS A MEANS TO FOSTER DIVERSITY AND INCLUSION IN THE U.S. CIVILIAN WORKFORCE ***   
. 
. 
. 
. *********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
.    
. *** MODEL G1.2: CONDITIONAL RESPONSES BY GENDER -- GENDER BETWEEN-IDENTITY GROUP STATUS DIFFERENTIAL MODEL: [WOMEN SUPERVISORS WITHIN AGENCY j IN YEAR t / MEN SUPERVISORS WITHIN AGENCY j IN YEAR t] / [WOMEN NON-SUPERVISORS WITHIN AGENCY j IN YEAR t / MEN NON-SUPERVISORS  WITHIN AGENCY j IN YEAR t]  -- CONTROLLING FOR GENDER SUPERVISORY EMPLOYEE IDENTITY GROUP DIFFERENTIAL ***
. 
. regress lndiversity2zeroadj  c.ln_ratio_fmsup_fmsub##i.gender   ln_ratio_fem_tot_men_tot  minority  supervisor  topoffgender_2 lntotworkforce_count  ln_professionals_total_ratio   i.agencyid i.year if ln_ratio_fmsup_fmsub <= 0, vce(cluster agencyid)

{txt}Linear regression                               Number of obs     = {res} 2,463,525
                                                {txt}{help j_robustsingular:F(17, 102) }       =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0401
                                                {txt}Root MSE          =    {res} .51461

{txt}{ralign 95:(Std. err. adjusted for {res:103} clusters in {res:agencyid})}
{hline 30}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 31}{c |}{col 43}    Robust
{col 1}          lndiversity2zeroadj{col 31}{c |} Coefficient{col 43}  std. err.{col 55}      t{col 63}   P>|t|{col 71}     [95% con{col 84}f. interval]
{hline 30}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}ln_ratio_fmsup_fmsub {c |}{col 31}{res}{space 2} .1504826{col 43}{space 2} .0431462{col 54}{space 1}    3.49{col 63}{space 3}0.001{col 71}{space 4} .0649024{col 84}{space 3} .2360629
{txt}{space 21}1.gender {c |}{col 31}{res}{space 2}-.0373166{col 43}{space 2} .0069919{col 54}{space 1}   -5.34{col 63}{space 3}0.000{col 71}{space 4} -.051185{col 84}{space 3}-.0234482
{txt}{space 29} {c |}
gender#c.ln_ratio_fmsup_fmsub {c |}
{space 27}1  {c |}{col 31}{res}{space 2} .0052303{col 43}{space 2} .0203914{col 54}{space 1}    0.26{col 63}{space 3}0.798{col 71}{space 4}-.0352159{col 84}{space 3} .0456765
{txt}{space 29} {c |}
{space 5}ln_ratio_fem_tot_men_tot {c |}{col 31}{res}{space 2}-.0072932{col 43}{space 2} .0557756{col 54}{space 1}   -0.13{col 63}{space 3}0.896{col 71}{space 4}-.1179239{col 84}{space 3} .1033376
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{txt}{space 26}98  {c |}{col 31}{res}{space 2} .5933256{col 43}{space 2} .1952936{col 54}{space 1}    3.04{col 63}{space 3}0.003{col 71}{space 4} .2059617{col 84}{space 3} .9806895
{txt}{space 26}99  {c |}{col 31}{res}{space 2} .0968216{col 43}{space 2} .0927488{col 54}{space 1}    1.04{col 63}{space 3}0.299{col 71}{space 4}-.0871452{col 84}{space 3} .2807884
{txt}{space 25}100  {c |}{col 31}{res}{space 2} .2807052{col 43}{space 2} .1550585{col 54}{space 1}    1.81{col 63}{space 3}0.073{col 71}{space 4}-.0268527{col 84}{space 3}  .588263
{txt}{space 25}101  {c |}{col 31}{res}{space 2} .4293165{col 43}{space 2}  .139966{col 54}{space 1}    3.07{col 63}{space 3}0.003{col 71}{space 4} .1516947{col 84}{space 3} .7069383
{txt}{space 25}103  {c |}{col 31}{res}{space 2} .1098417{col 43}{space 2} .1088869{col 54}{space 1}    1.01{col 63}{space 3}0.315{col 71}{space 4} -.106135{col 84}{space 3} .3258185
{txt}{space 25}104  {c |}{col 31}{res}{space 2}-.1013807{col 43}{space 2} .0765073{col 54}{space 1}   -1.33{col 63}{space 3}0.188{col 71}{space 4}-.2531327{col 84}{space 3} .0503712
{txt}{space 25}105  {c |}{col 31}{res}{space 2} .3645497{col 43}{space 2} .1608023{col 54}{space 1}    2.27{col 63}{space 3}0.025{col 71}{space 4}  .045599{col 84}{space 3} .6835003
{txt}{space 29} {c |}
{space 25}year {c |}
{space 24}2011  {c |}{col 31}{res}{space 2}-.0043184{col 43}{space 2} .0032412{col 54}{space 1}   -1.33{col 63}{space 3}0.186{col 71}{space 4}-.0107473{col 84}{space 3} .0021105
{txt}{space 24}2012  {c |}{col 31}{res}{space 2}-.0002939{col 43}{space 2} .0045211{col 54}{space 1}   -0.07{col 63}{space 3}0.948{col 71}{space 4}-.0092614{col 84}{space 3} .0086736
{txt}{space 24}2013  {c |}{col 31}{res}{space 2}-.0015665{col 43}{space 2} .0049822{col 54}{space 1}   -0.31{col 63}{space 3}0.754{col 71}{space 4}-.0114487{col 84}{space 3} .0083158
{txt}{space 24}2014  {c |}{col 31}{res}{space 2}-.0072385{col 43}{space 2} .0070847{col 54}{space 1}   -1.02{col 63}{space 3}0.309{col 71}{space 4} -.021291{col 84}{space 3}  .006814
{txt}{space 24}2015  {c |}{col 31}{res}{space 2}-.0142523{col 43}{space 2} .0087504{col 54}{space 1}   -1.63{col 63}{space 3}0.106{col 71}{space 4}-.0316087{col 84}{space 3} .0031041
{txt}{space 24}2016  {c |}{col 31}{res}{space 2}-.0180027{col 43}{space 2} .0075968{col 54}{space 1}   -2.37{col 63}{space 3}0.020{col 71}{space 4}-.0330709{col 84}{space 3}-.0029344
{txt}{space 24}2017  {c |}{col 31}{res}{space 2}  -.00348{col 43}{space 2} .0098193{col 54}{space 1}   -0.35{col 63}{space 3}0.724{col 71}{space 4}-.0229566{col 84}{space 3} .0159966
{txt}{space 24}2018  {c |}{col 31}{res}{space 2}-.0329572{col 43}{space 2} .0074121{col 54}{space 1}   -4.45{col 63}{space 3}0.000{col 71}{space 4}-.0476591{col 84}{space 3}-.0182553
{txt}{space 24}2019  {c |}{col 31}{res}{space 2}-.0723152{col 43}{space 2} .0085115{col 54}{space 1}   -8.50{col 63}{space 3}0.000{col 71}{space 4}-.0891978{col 84}{space 3}-.0554326
{txt}{space 29} {c |}
{space 24}_cons {c |}{col 31}{res}{space 2}-.2033228{col 43}{space 2} .4396854{col 54}{space 1}   -0.46{col 63}{space 3}0.645{col 71}{space 4}-1.075437{col 84}{space 3}  .668791
{txt}{hline 30}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16} 2,463,525{col 28} -1909279{col 39} -1858897{col 50}    18{col 58}  3717831{col 69}  3718059
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] IC note}}.{p_end}

{com}. *
. ** BETWEEN-IDENTITY "IN" GROUP VERSUS "OUT" GROUP STATUS DIFFERENTIAL BETWEEN GENDERED RESPONDENTS **
. 
. lincom c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .1504826{col 26}{space 2} .0431462{col 37}{space 1}    3.49{col 46}{space 3}0.001{col 54}{space 4} .0649024{col 67}{space 3} .2360629
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  1.gender#c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.gender#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0052303{col 26}{space 2} .0203914{col 37}{space 1}    0.26{col 46}{space 3}0.798{col 54}{space 4}-.0352159{col 67}{space 3} .0456765
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. *
. 
.    
. *** MODEL G2.2: CONDITIONAL RESPONSES BY RACE/ETHNICITY -- RACIAL/ETHNIC BETWEEN-IDENTITY GROUP STATUS DIFFERENTIAL MODEL: [MINORITY SUPERVISORS WITHIN AGENCY j IN YEAR t / NON-MINORITY SUPERVISORS WITHIN AGENCY j IN YEAR t] / [MINORITY NON-SUPERVISORS WITHIN AGENCY j IN YEAR t / NON-MINORITY NON-SUPERVISORS  WITHIN AGENCY j IN YEAR t] -- CONTROLLING FOR RACIAL/ETHNIC SUPERVISORY EMPLOYEE IDENTITY GROUP DIFFERENTIAL ***
. 
. regress  lndiversity2zeroadj  c.ln_ratio_mnmsup_mnmsub##i.minority   ln_ratio_min_tot_nmin_tot   gender supervisor  topoffminority_2 lntotworkforce_count  ln_professionals_total_ratio  i.agencyid i.year if ln_ratio_mnmsup_mnmsub<=0, vce(cluster agencyid)

{txt}Linear regression                               Number of obs     = {res} 2,454,819
                                                {txt}{help j_robustsingular:F(17, 101) }       =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0404
                                                {txt}Root MSE          =    {res} .51627

{txt}{ralign 99:(Std. err. adjusted for {res:102} clusters in {res:agencyid})}
{hline 34}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 35}{c |}{col 47}    Robust
{col 1}              lndiversity2zeroadj{col 35}{c |} Coefficient{col 47}  std. err.{col 59}      t{col 67}   P>|t|{col 75}     [95% con{col 88}f. interval]
{hline 34}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}ln_ratio_mnmsup_mnmsub {c |}{col 35}{res}{space 2}  .055562{col 47}{space 2} .0362562{col 58}{space 1}    1.53{col 67}{space 3}0.129{col 75}{space 4}-.0163605{col 88}{space 3} .1274845
{txt}{space 23}1.minority {c |}{col 35}{res}{space 2}-.0747755{col 47}{space 2} .0068295{col 58}{space 1}  -10.95{col 67}{space 3}0.000{col 75}{space 4}-.0883233{col 88}{space 3}-.0612277
{txt}{space 33} {c |}
minority#c.ln_ratio_mnmsup_mnmsub {c |}
{space 31}1  {c |}{col 35}{res}{space 2} .0553309{col 47}{space 2} .0188897{col 58}{space 1}    2.93{col 67}{space 3}0.004{col 75}{space 4} .0178588{col 88}{space 3} .0928031
{txt}{space 33} {c |}
{space 8}ln_ratio_min_tot_nmin_tot {c |}{col 35}{res}{space 2} .0572841{col 47}{space 2} .0472636{col 58}{space 1}    1.21{col 67}{space 3}0.228{col 75}{space 4}-.0364742{col 88}{space 3} .1510424
{txt}{space 27}gender {c |}{col 35}{res}{space 2}-.0395985{col 47}{space 2} .0042311{col 58}{space 1}   -9.36{col 67}{space 3}0.000{col 75}{space 4}-.0479918{col 88}{space 3}-.0312052
{txt}{space 23}supervisor {c |}{col 35}{res}{space 2} .1283514{col 47}{space 2} .0076246{col 58}{space 1}   16.83{col 67}{space 3}0.000{col 75}{space 4} .1132263{col 88}{space 3} .1434765
{txt}{space 17}topoffminority_2 {c |}{col 35}{res}{space 2} .0082235{col 47}{space 2} .0059071{col 58}{space 1}    1.39{col 67}{space 3}0.167{col 75}{space 4}-.0034946{col 88}{space 3} .0199415
{txt}{space 13}lntotworkforce_count {c |}{col 35}{res}{space 2} .0653612{col 47}{space 2} .0420359{col 58}{space 1}    1.55{col 67}{space 3}0.123{col 75}{space 4}-.0180267{col 88}{space 3} .1487491
{txt}{space 5}ln_professionals_total_ratio {c |}{col 35}{res}{space 2} .0208961{col 47}{space 2} .0522911{col 58}{space 1}    0.40{col 67}{space 3}0.690{col 75}{space 4}-.0828355{col 88}{space 3} .1246276
{txt}{space 33} {c |}
{space 25}agencyid {c |}
{space 31}2  {c |}{col 35}{res}{space 2} .2138514{col 47}{space 2} .1292123{col 58}{space 1}    1.66{col 67}{space 3}0.101{col 75}{space 4}-.0424709{col 88}{space 3} .4701738
{txt}{space 31}4  {c |}{col 35}{res}{space 2} .2854612{col 47}{space 2}  .168439{col 58}{space 1}    1.69{col 67}{space 3}0.093{col 75}{space 4}-.0486764{col 88}{space 3} .6195988
{txt}{space 31}5  {c |}{col 35}{res}{space 2} .1670361{col 47}{space 2} .1330309{col 58}{space 1}    1.26{col 67}{space 3}0.212{col 75}{space 4}-.0968613{col 88}{space 3} .4309335
{txt}{space 31}7  {c |}{col 35}{res}{space 2} .2433573{col 47}{space 2} .2269766{col 58}{space 1}    1.07{col 67}{space 3}0.286{col 75}{space 4}-.2069032{col 88}{space 3} .6936178
{txt}{space 31}8  {c |}{col 35}{res}{space 2} .2340647{col 47}{space 2} .1701054{col 58}{space 1}    1.38{col 67}{space 3}0.172{col 75}{space 4}-.1033786{col 88}{space 3}  .571508
{txt}{space 31}9  {c |}{col 35}{res}{space 2}-.0567722{col 47}{space 2} .0233305{col 58}{space 1}   -2.43{col 67}{space 3}0.017{col 75}{space 4}-.1030536{col 88}{space 3}-.0104909
{txt}{space 30}10  {c |}{col 35}{res}{space 2} .1907127{col 47}{space 2} .2394914{col 58}{space 1}    0.80{col 67}{space 3}0.428{col 75}{space 4}-.2843738{col 88}{space 3} .6657993
{txt}{space 30}11  {c |}{col 35}{res}{space 2} .1662066{col 47}{space 2} .1104656{col 58}{space 1}    1.50{col 67}{space 3}0.136{col 75}{space 4}-.0529274{col 88}{space 3} .3853407
{txt}{space 30}12  {c |}{col 35}{res}{space 2} .3185237{col 47}{space 2} .2214116{col 58}{space 1}    1.44{col 67}{space 3}0.153{col 75}{space 4}-.1206973{col 88}{space 3} .7577447
{txt}{space 30}13  {c |}{col 35}{res}{space 2} .3181461{col 47}{space 2} .1713132{col 58}{space 1}    1.86{col 67}{space 3}0.066{col 75}{space 4}-.0216932{col 88}{space 3} .6579853
{txt}{space 30}14  {c |}{col 35}{res}{space 2} .1608722{col 47}{space 2} .1169695{col 58}{space 1}    1.38{col 67}{space 3}0.172{col 75}{space 4}-.0711639{col 88}{space 3} .3929083
{txt}{space 30}15  {c |}{col 35}{res}{space 2} .2314595{col 47}{space 2} .1622441{col 58}{space 1}    1.43{col 67}{space 3}0.157{col 75}{space 4}-.0903892{col 88}{space 3} .5533082
{txt}{space 30}16  {c |}{col 35}{res}{space 2} .1945092{col 47}{space 2} .3017479{col 58}{space 1}    0.64{col 67}{space 3}0.521{col 75}{space 4}-.4040774{col 88}{space 3} .7930958
{txt}{space 30}17  {c |}{col 35}{res}{space 2} .0388474{col 47}{space 2} .1955695{col 58}{space 1}    0.20{col 67}{space 3}0.843{col 75}{space 4}  -.34911{col 88}{space 3} .4268047
{txt}{space 30}18  {c |}{col 35}{res}{space 2} .3121791{col 47}{space 2} .1780889{col 58}{space 1}    1.75{col 67}{space 3}0.083{col 75}{space 4}-.0411013{col 88}{space 3} .6654595
{txt}{space 30}19  {c |}{col 35}{res}{space 2} .1793692{col 47}{space 2} .1209539{col 58}{space 1}    1.48{col 67}{space 3}0.141{col 75}{space 4}-.0605708{col 88}{space 3} .4193091
{txt}{space 30}20  {c |}{col 35}{res}{space 2} .0847813{col 47}{space 2}  .126442{col 58}{space 1}    0.67{col 67}{space 3}0.504{col 75}{space 4}-.1660457{col 88}{space 3} .3356082
{txt}{space 30}21  {c |}{col 35}{res}{space 2} .2113312{col 47}{space 2}  .115219{col 58}{space 1}    1.83{col 67}{space 3}0.070{col 75}{space 4}-.0172323{col 88}{space 3} .4398946
{txt}{space 30}22  {c |}{col 35}{res}{space 2} .1468741{col 47}{space 2} .1822031{col 58}{space 1}    0.81{col 67}{space 3}0.422{col 75}{space 4}-.2145678{col 88}{space 3} .5083161
{txt}{space 30}23  {c |}{col 35}{res}{space 2} -.109442{col 47}{space 2} .0923838{col 58}{space 1}   -1.18{col 67}{space 3}0.239{col 75}{space 4}-.2927067{col 88}{space 3} .0738227
{txt}{space 30}24  {c |}{col 35}{res}{space 2} .2488663{col 47}{space 2} .1276125{col 58}{space 1}    1.95{col 67}{space 3}0.054{col 75}{space 4}-.0042825{col 88}{space 3} .5020152
{txt}{space 30}25  {c |}{col 35}{res}{space 2}  .184792{col 47}{space 2} .1543194{col 58}{space 1}    1.20{col 67}{space 3}0.234{col 75}{space 4}-.1213361{col 88}{space 3} .4909202
{txt}{space 30}26  {c |}{col 35}{res}{space 2} .0499825{col 47}{space 2} .1359186{col 58}{space 1}    0.37{col 67}{space 3}0.714{col 75}{space 4}-.2196435{col 88}{space 3} .3196085
{txt}{space 30}27  {c |}{col 35}{res}{space 2} .3604754{col 47}{space 2} .2046783{col 58}{space 1}    1.76{col 67}{space 3}0.081{col 75}{space 4}-.0455512{col 88}{space 3}  .766502
{txt}{space 30}28  {c |}{col 35}{res}{space 2}-.0530228{col 47}{space 2} .1162823{col 58}{space 1}   -0.46{col 67}{space 3}0.649{col 75}{space 4}-.2836957{col 88}{space 3}   .17765
{txt}{space 30}29  {c |}{col 35}{res}{space 2} .0791665{col 47}{space 2} .1941918{col 58}{space 1}    0.41{col 67}{space 3}0.684{col 75}{space 4}-.3060578{col 88}{space 3} .4643909
{txt}{space 30}30  {c |}{col 35}{res}{space 2}  .157685{col 47}{space 2} .1788098{col 58}{space 1}    0.88{col 67}{space 3}0.380{col 75}{space 4}-.1970256{col 88}{space 3} .5123955
{txt}{space 30}31  {c |}{col 35}{res}{space 2}-.0254712{col 47}{space 2} .1872522{col 58}{space 1}   -0.14{col 67}{space 3}0.892{col 75}{space 4}-.3969293{col 88}{space 3} .3459868
{txt}{space 30}32  {c |}{col 35}{res}{space 2} .2059905{col 47}{space 2} .1489936{col 58}{space 1}    1.38{col 67}{space 3}0.170{col 75}{space 4}-.0895727{col 88}{space 3} .5015537
{txt}{space 30}33  {c |}{col 35}{res}{space 2} .1181463{col 47}{space 2} .0937428{col 58}{space 1}    1.26{col 67}{space 3}0.210{col 75}{space 4}-.0678142{col 88}{space 3} .3041068
{txt}{space 30}34  {c |}{col 35}{res}{space 2}-.1611685{col 47}{space 2} .2388374{col 58}{space 1}   -0.67{col 67}{space 3}0.501{col 75}{space 4}-.6349577{col 88}{space 3} .3126207
{txt}{space 30}35  {c |}{col 35}{res}{space 2} .1430291{col 47}{space 2} .1077222{col 58}{space 1}    1.33{col 67}{space 3}0.187{col 75}{space 4}-.0706627{col 88}{space 3} .3567209
{txt}{space 30}36  {c |}{col 35}{res}{space 2} .2151728{col 47}{space 2} .1420908{col 58}{space 1}    1.51{col 67}{space 3}0.133{col 75}{space 4}-.0666971{col 88}{space 3} .4970426
{txt}{space 30}37  {c |}{col 35}{res}{space 2}  .201121{col 47}{space 2} .1220032{col 58}{space 1}    1.65{col 67}{space 3}0.102{col 75}{space 4}-.0409006{col 88}{space 3} .4431425
{txt}{space 30}38  {c |}{col 35}{res}{space 2} .3112324{col 47}{space 2} .2059694{col 58}{space 1}    1.51{col 67}{space 3}0.134{col 75}{space 4}-.0973555{col 88}{space 3} .7198204
{txt}{space 30}39  {c |}{col 35}{res}{space 2} .2215384{col 47}{space 2} .1251641{col 58}{space 1}    1.77{col 67}{space 3}0.080{col 75}{space 4}-.0267534{col 88}{space 3} .4698303
{txt}{space 30}40  {c |}{col 35}{res}{space 2} .0448339{col 47}{space 2} .0807886{col 58}{space 1}    0.55{col 67}{space 3}0.580{col 75}{space 4} -.115429{col 88}{space 3} .2050969
{txt}{space 30}41  {c |}{col 35}{res}{space 2} .1809099{col 47}{space 2} .1800485{col 58}{space 1}    1.00{col 67}{space 3}0.317{col 75}{space 4}-.1762579{col 88}{space 3} .5380776
{txt}{space 30}42  {c |}{col 35}{res}{space 2} .2843044{col 47}{space 2}  .164644{col 58}{space 1}    1.73{col 67}{space 3}0.087{col 75}{space 4}-.0423051{col 88}{space 3} .6109139
{txt}{space 30}43  {c |}{col 35}{res}{space 2} .0036153{col 47}{space 2}  .077655{col 58}{space 1}    0.05{col 67}{space 3}0.963{col 75}{space 4}-.1504314{col 88}{space 3}  .157662
{txt}{space 30}44  {c |}{col 35}{res}{space 2} .2566343{col 47}{space 2} .1420356{col 58}{space 1}    1.81{col 67}{space 3}0.074{col 75}{space 4}-.0251262{col 88}{space 3} .5383947
{txt}{space 30}45  {c |}{col 35}{res}{space 2} .2220146{col 47}{space 2} .1273604{col 58}{space 1}    1.74{col 67}{space 3}0.084{col 75}{space 4}-.0306341{col 88}{space 3} .4746634
{txt}{space 30}47  {c |}{col 35}{res}{space 2} .1523038{col 47}{space 2} .0951281{col 58}{space 1}    1.60{col 67}{space 3}0.112{col 75}{space 4}-.0364049{col 88}{space 3} .3410124
{txt}{space 30}48  {c |}{col 35}{res}{space 2} .2365224{col 47}{space 2} .1945391{col 58}{space 1}    1.22{col 67}{space 3}0.227{col 75}{space 4} -.149391{col 88}{space 3} .6224357
{txt}{space 30}49  {c |}{col 35}{res}{space 2} .3404167{col 47}{space 2} .2134532{col 58}{space 1}    1.59{col 67}{space 3}0.114{col 75}{space 4} -.083017{col 88}{space 3} .7638505
{txt}{space 30}50  {c |}{col 35}{res}{space 2} .3473633{col 47}{space 2} .1942325{col 58}{space 1}    1.79{col 67}{space 3}0.077{col 75}{space 4}-.0379417{col 88}{space 3} .7326682
{txt}{space 30}51  {c |}{col 35}{res}{space 2} .3398002{col 47}{space 2} .2401446{col 58}{space 1}    1.41{col 67}{space 3}0.160{col 75}{space 4} -.136582{col 88}{space 3} .8161825
{txt}{space 30}52  {c |}{col 35}{res}{space 2} .2341894{col 47}{space 2} .2413711{col 58}{space 1}    0.97{col 67}{space 3}0.334{col 75}{space 4}-.2446259{col 88}{space 3} .7130047
{txt}{space 30}53  {c |}{col 35}{res}{space 2} .2817615{col 47}{space 2} .1719936{col 58}{space 1}    1.64{col 67}{space 3}0.104{col 75}{space 4}-.0594275{col 88}{space 3} .6229505
{txt}{space 30}54  {c |}{col 35}{res}{space 2} .2941813{col 47}{space 2} .1928189{col 58}{space 1}    1.53{col 67}{space 3}0.130{col 75}{space 4}-.0883194{col 88}{space 3} .6766821
{txt}{space 30}55  {c |}{col 35}{res}{space 2} .4216087{col 47}{space 2} .2472859{col 58}{space 1}    1.70{col 67}{space 3}0.091{col 75}{space 4}  -.06894{col 88}{space 3} .9121574
{txt}{space 30}56  {c |}{col 35}{res}{space 2} .2453773{col 47}{space 2}  .249449{col 58}{space 1}    0.98{col 67}{space 3}0.328{col 75}{space 4}-.2494624{col 88}{space 3}  .740217
{txt}{space 30}57  {c |}{col 35}{res}{space 2} .3093859{col 47}{space 2} .3149615{col 58}{space 1}    0.98{col 67}{space 3}0.328{col 75}{space 4} -.315413{col 88}{space 3} .9341848
{txt}{space 30}58  {c |}{col 35}{res}{space 2} .1982084{col 47}{space 2} .1824679{col 58}{space 1}    1.09{col 67}{space 3}0.280{col 75}{space 4}-.1637588{col 88}{space 3} .5601756
{txt}{space 30}59  {c |}{col 35}{res}{space 2} .1898784{col 47}{space 2}   .21995{col 58}{space 1}    0.86{col 67}{space 3}0.390{col 75}{space 4}-.2464432{col 88}{space 3} .6262001
{txt}{space 30}60  {c |}{col 35}{res}{space 2} .1469842{col 47}{space 2} .1093722{col 58}{space 1}    1.34{col 67}{space 3}0.182{col 75}{space 4}-.0699808{col 88}{space 3} .3639493
{txt}{space 30}61  {c |}{col 35}{res}{space 2} .1376899{col 47}{space 2} .1166665{col 58}{space 1}    1.18{col 67}{space 3}0.241{col 75}{space 4} -.093745{col 88}{space 3} .3691247
{txt}{space 30}62  {c |}{col 35}{res}{space 2} .3238364{col 47}{space 2} .2166274{col 58}{space 1}    1.49{col 67}{space 3}0.138{col 75}{space 4}-.1058941{col 88}{space 3} .7535668
{txt}{space 30}63  {c |}{col 35}{res}{space 2} .3913994{col 47}{space 2}  .211741{col 58}{space 1}    1.85{col 67}{space 3}0.067{col 75}{space 4}-.0286378{col 88}{space 3} .8114367
{txt}{space 30}64  {c |}{col 35}{res}{space 2} .4046157{col 47}{space 2} .2221109{col 58}{space 1}    1.82{col 67}{space 3}0.071{col 75}{space 4}-.0359927{col 88}{space 3}  .845224
{txt}{space 30}65  {c |}{col 35}{res}{space 2} .2117676{col 47}{space 2} .1270989{col 58}{space 1}    1.67{col 67}{space 3}0.099{col 75}{space 4}-.0403623{col 88}{space 3} .4638976
{txt}{space 30}66  {c |}{col 35}{res}{space 2} .1904316{col 47}{space 2} .2351339{col 58}{space 1}    0.81{col 67}{space 3}0.420{col 75}{space 4}-.2760109{col 88}{space 3} .6568741
{txt}{space 30}67  {c |}{col 35}{res}{space 2} .2261495{col 47}{space 2} .1431592{col 58}{space 1}    1.58{col 67}{space 3}0.117{col 75}{space 4}-.0578398{col 88}{space 3} .5101388
{txt}{space 30}68  {c |}{col 35}{res}{space 2} .2811672{col 47}{space 2} .1589943{col 58}{space 1}    1.77{col 67}{space 3}0.080{col 75}{space 4}-.0342347{col 88}{space 3}  .596569
{txt}{space 30}69  {c |}{col 35}{res}{space 2} .1413023{col 47}{space 2} .1293078{col 58}{space 1}    1.09{col 67}{space 3}0.277{col 75}{space 4}-.1152096{col 88}{space 3} .3978142
{txt}{space 30}70  {c |}{col 35}{res}{space 2} .3476868{col 47}{space 2} .2194768{col 58}{space 1}    1.58{col 67}{space 3}0.116{col 75}{space 4} -.087696{col 88}{space 3} .7830697
{txt}{space 30}71  {c |}{col 35}{res}{space 2}-.1184102{col 47}{space 2} .1912358{col 58}{space 1}   -0.62{col 67}{space 3}0.537{col 75}{space 4}-.4977705{col 88}{space 3} .2609501
{txt}{space 30}72  {c |}{col 35}{res}{space 2} .2027942{col 47}{space 2}  .124234{col 58}{space 1}    1.63{col 67}{space 3}0.106{col 75}{space 4}-.0436527{col 88}{space 3} .4492411
{txt}{space 30}73  {c |}{col 35}{res}{space 2} .4285362{col 47}{space 2} .2018782{col 58}{space 1}    2.12{col 67}{space 3}0.036{col 75}{space 4} .0280641{col 88}{space 3} .8290082
{txt}{space 30}74  {c |}{col 35}{res}{space 2} .1570119{col 47}{space 2} .1068222{col 58}{space 1}    1.47{col 67}{space 3}0.145{col 75}{space 4}-.0548945{col 88}{space 3} .3689183
{txt}{space 30}75  {c |}{col 35}{res}{space 2} .0647635{col 47}{space 2} .1576072{col 58}{space 1}    0.41{col 67}{space 3}0.682{col 75}{space 4}-.2478867{col 88}{space 3} .3774138
{txt}{space 30}76  {c |}{col 35}{res}{space 2} .2988142{col 47}{space 2} .1821571{col 58}{space 1}    1.64{col 67}{space 3}0.104{col 75}{space 4}-.0625366{col 88}{space 3}  .660165
{txt}{space 30}77  {c |}{col 35}{res}{space 2} .2232043{col 47}{space 2} .1783574{col 58}{space 1}    1.25{col 67}{space 3}0.214{col 75}{space 4}-.1306088{col 88}{space 3} .5770173
{txt}{space 30}78  {c |}{col 35}{res}{space 2} .2702887{col 47}{space 2} .1161384{col 58}{space 1}    2.33{col 67}{space 3}0.022{col 75}{space 4} .0399014{col 88}{space 3} .5006759
{txt}{space 30}79  {c |}{col 35}{res}{space 2}-.0175747{col 47}{space 2} .0258493{col 58}{space 1}   -0.68{col 67}{space 3}0.498{col 75}{space 4}-.0688528{col 88}{space 3} .0337035
{txt}{space 30}80  {c |}{col 35}{res}{space 2}   .43696{col 47}{space 2} .2249807{col 58}{space 1}    1.94{col 67}{space 3}0.055{col 75}{space 4}-.0093413{col 88}{space 3} .8832612
{txt}{space 30}81  {c |}{col 35}{res}{space 2} .1634691{col 47}{space 2} .2484475{col 58}{space 1}    0.66{col 67}{space 3}0.512{col 75}{space 4}-.3293839{col 88}{space 3} .6563222
{txt}{space 30}82  {c |}{col 35}{res}{space 2} .2365299{col 47}{space 2} .2144023{col 58}{space 1}    1.10{col 67}{space 3}0.273{col 75}{space 4}-.1887866{col 88}{space 3} .6618463
{txt}{space 30}83  {c |}{col 35}{res}{space 2} .3474903{col 47}{space 2} .1780204{col 58}{space 1}    1.95{col 67}{space 3}0.054{col 75}{space 4}-.0056542{col 88}{space 3} .7006348
{txt}{space 30}84  {c |}{col 35}{res}{space 2} .2861957{col 47}{space 2} .2178301{col 58}{space 1}    1.31{col 67}{space 3}0.192{col 75}{space 4}-.1459205{col 88}{space 3} .7183119
{txt}{space 30}85  {c |}{col 35}{res}{space 2} .3030258{col 47}{space 2} .1674561{col 58}{space 1}    1.81{col 67}{space 3}0.073{col 75}{space 4} -.029162{col 88}{space 3} .6352137
{txt}{space 30}86  {c |}{col 35}{res}{space 2} .3257048{col 47}{space 2} .2519421{col 58}{space 1}    1.29{col 67}{space 3}0.199{col 75}{space 4}-.1740805{col 88}{space 3} .8254901
{txt}{space 30}87  {c |}{col 35}{res}{space 2} .3831278{col 47}{space 2} .2492456{col 58}{space 1}    1.54{col 67}{space 3}0.127{col 75}{space 4}-.1113084{col 88}{space 3} .8775641
{txt}{space 30}88  {c |}{col 35}{res}{space 2} .1681418{col 47}{space 2} .1760221{col 58}{space 1}    0.96{col 67}{space 3}0.342{col 75}{space 4}-.1810387{col 88}{space 3} .5173222
{txt}{space 30}89  {c |}{col 35}{res}{space 2} .2324507{col 47}{space 2} .1730237{col 58}{space 1}    1.34{col 67}{space 3}0.182{col 75}{space 4}-.1107818{col 88}{space 3} .5756831
{txt}{space 30}90  {c |}{col 35}{res}{space 2} .0148863{col 47}{space 2} .0888796{col 58}{space 1}    0.17{col 67}{space 3}0.867{col 75}{space 4} -.161427{col 88}{space 3} .1911995
{txt}{space 30}91  {c |}{col 35}{res}{space 2} .1413665{col 47}{space 2} .1240089{col 58}{space 1}    1.14{col 67}{space 3}0.257{col 75}{space 4}-.1046338{col 88}{space 3} .3873668
{txt}{space 30}92  {c |}{col 35}{res}{space 2}  .079447{col 47}{space 2} .0620387{col 58}{space 1}    1.28{col 67}{space 3}0.203{col 75}{space 4}-.0436211{col 88}{space 3} .2025151
{txt}{space 30}93  {c |}{col 35}{res}{space 2} .3560516{col 47}{space 2} .1820893{col 58}{space 1}    1.96{col 67}{space 3}0.053{col 75}{space 4}-.0051646{col 88}{space 3} .7172678
{txt}{space 30}94  {c |}{col 35}{res}{space 2} .4530462{col 47}{space 2} .2208275{col 58}{space 1}    2.05{col 67}{space 3}0.043{col 75}{space 4} .0149838{col 88}{space 3} .8911086
{txt}{space 30}95  {c |}{col 35}{res}{space 2} .1656264{col 47}{space 2} .2177304{col 58}{space 1}    0.76{col 67}{space 3}0.449{col 75}{space 4}-.2662921{col 88}{space 3} .5975449
{txt}{space 30}96  {c |}{col 35}{res}{space 2} .2988972{col 47}{space 2} .1962336{col 58}{space 1}    1.52{col 67}{space 3}0.131{col 75}{space 4}-.0903775{col 88}{space 3} .6881718
{txt}{space 30}97  {c |}{col 35}{res}{space 2} .3336955{col 47}{space 2} .1610505{col 58}{space 1}    2.07{col 67}{space 3}0.041{col 75}{space 4} .0142146{col 88}{space 3} .6531765
{txt}{space 30}98  {c |}{col 35}{res}{space 2} .4440741{col 47}{space 2} .2323899{col 58}{space 1}    1.91{col 67}{space 3}0.059{col 75}{space 4} -.016925{col 88}{space 3} .9050731
{txt}{space 30}99  {c |}{col 35}{res}{space 2} .0414179{col 47}{space 2}  .059095{col 58}{space 1}    0.70{col 67}{space 3}0.485{col 75}{space 4}-.0758107{col 88}{space 3} .1586464
{txt}{space 29}100  {c |}{col 35}{res}{space 2} .2074935{col 47}{space 2} .2190518{col 58}{space 1}    0.95{col 67}{space 3}0.346{col 75}{space 4}-.2270464{col 88}{space 3} .6420334
{txt}{space 29}101  {c |}{col 35}{res}{space 2} .3685455{col 47}{space 2} .1729731{col 58}{space 1}    2.13{col 67}{space 3}0.036{col 75}{space 4} .0254134{col 88}{space 3} .7116777
{txt}{space 29}102  {c |}{col 35}{res}{space 2} .2753711{col 47}{space 2} .2215363{col 58}{space 1}    1.24{col 67}{space 3}0.217{col 75}{space 4}-.1640974{col 88}{space 3} .7148396
{txt}{space 29}103  {c |}{col 35}{res}{space 2} .0780875{col 47}{space 2}  .121921{col 58}{space 1}    0.64{col 67}{space 3}0.523{col 75}{space 4}-.1637709{col 88}{space 3}  .319946
{txt}{space 29}104  {c |}{col 35}{res}{space 2}-.1601169{col 47}{space 2} .0612419{col 58}{space 1}   -2.61{col 67}{space 3}0.010{col 75}{space 4}-.2816044{col 88}{space 3}-.0386293
{txt}{space 29}105  {c |}{col 35}{res}{space 2} .2272693{col 47}{space 2} .2080719{col 58}{space 1}    1.09{col 67}{space 3}0.277{col 75}{space 4}-.1854895{col 88}{space 3}  .640028
{txt}{space 33} {c |}
{space 29}year {c |}
{space 28}2011  {c |}{col 35}{res}{space 2}-.0018532{col 47}{space 2} .0038743{col 58}{space 1}   -0.48{col 67}{space 3}0.633{col 75}{space 4}-.0095387{col 88}{space 3} .0058324
{txt}{space 28}2012  {c |}{col 35}{res}{space 2} .0054251{col 47}{space 2} .0046622{col 58}{space 1}    1.16{col 67}{space 3}0.247{col 75}{space 4}-.0038235{col 88}{space 3} .0146738
{txt}{space 28}2013  {c |}{col 35}{res}{space 2} .0030614{col 47}{space 2}  .005789{col 58}{space 1}    0.53{col 67}{space 3}0.598{col 75}{space 4}-.0084224{col 88}{space 3} .0145453
{txt}{space 28}2014  {c |}{col 35}{res}{space 2}-.0021911{col 47}{space 2} .0068734{col 58}{space 1}   -0.32{col 67}{space 3}0.751{col 75}{space 4}-.0158261{col 88}{space 3} .0114439
{txt}{space 28}2015  {c |}{col 35}{res}{space 2}-.0103116{col 47}{space 2} .0083099{col 58}{space 1}   -1.24{col 67}{space 3}0.218{col 75}{space 4}-.0267963{col 88}{space 3}  .006173
{txt}{space 28}2016  {c |}{col 35}{res}{space 2}-.0131322{col 47}{space 2} .0082553{col 58}{space 1}   -1.59{col 67}{space 3}0.115{col 75}{space 4}-.0295086{col 88}{space 3} .0032442
{txt}{space 28}2017  {c |}{col 35}{res}{space 2}-.0030054{col 47}{space 2} .0120769{col 58}{space 1}   -0.25{col 67}{space 3}0.804{col 75}{space 4}-.0269627{col 88}{space 3} .0209519
{txt}{space 28}2018  {c |}{col 35}{res}{space 2}-.0291455{col 47}{space 2} .0110542{col 58}{space 1}   -2.64{col 67}{space 3}0.010{col 75}{space 4}-.0510741{col 88}{space 3}-.0072169
{txt}{space 28}2019  {c |}{col 35}{res}{space 2}-.0684656{col 47}{space 2}  .012453{col 58}{space 1}   -5.50{col 67}{space 3}0.000{col 75}{space 4}-.0931691{col 88}{space 3}-.0437621
{txt}{space 33} {c |}
{space 28}_cons {c |}{col 35}{res}{space 2} .0419443{col 47}{space 2} .5325422{col 58}{space 1}    0.08{col 67}{space 3}0.937{col 75}{space 4}-1.014476{col 88}{space 3} 1.098365
{txt}{hline 34}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16} 2,454,819{col 28} -1910867{col 39} -1860224{col 50}    18{col 58}  3720485{col 69}  3720714
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] IC note}}.{p_end}

{com}. *
. ** BETWEEN-IDENTITY "IN" GROUP VERSUS "OUT" GROUP STATUS DIFFERENTIAL BETWEEN MINORITY/NON-MINORITY RESPONDENTS **
. 
. lincom c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}  .055562{col 26}{space 2} .0362562{col 37}{space 1}    1.53{col 46}{space 3}0.129{col 54}{space 4}-.0163605{col 67}{space 3} .1274845
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  1.minority#c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.minority#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0553309{col 26}{space 2} .0188897{col 37}{space 1}    2.93{col 46}{space 3}0.004{col 54}{space 4} .0178588{col 67}{space 3} .0928031
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. *
. *
. *
. 
.   
. *** MODEL G3.2: CONDITIONAL RESPONSES BY MINORITY WOMEN VERSUS WHITE WOMEN [BASELINE CATEGORY: MEN RESPONDENTS] -- GENDER BETWEEN-IDENTITY GROUP STATUS DIFFERENTIAL MODEL: [WOMEN SUPERVISORS WITHIN AGENCY j IN YEAR t / MEN SUPERVISORS WITHIN AGENCY j IN YEAR t] / [WOMEN NON-SUPERVISORS WITHIN AGENCY j IN YEAR t / MEN NON-SUPERVISORS WITHIN AGENCY j IN YEAR t]  -- CONTROLLING FOR GENDER SUPERVISORY EMPLOYEE IDENTITY GROUP DIFFERENTIAL ***
. 
. regress lndiversity2zeroadj  c.ln_ratio_fmsup_fmsub##i.women_het   ln_ratio_fem_tot_men_tot  minority  supervisor  topoffgender_2 lntotworkforce_count  ln_professionals_total_ratio  i.agencyid i.year if ln_ratio_fmsup_fmsub <= 0, vce(cluster agencyid)

{txt}Linear regression                               Number of obs     = {res} 2,463,525
                                                {txt}{help j_robustsingular:F(19, 102) }       =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0404
                                                {txt}Root MSE          =    {res} .51454

{txt}{ralign 98:(Std. err. adjusted for {res:103} clusters in {res:agencyid})}
{hline 33}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 34}{c |}{col 46}    Robust
{col 1}             lndiversity2zeroadj{col 34}{c |} Coefficient{col 46}  std. err.{col 58}      t{col 66}   P>|t|{col 74}     [95% con{col 87}f. interval]
{hline 33}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}ln_ratio_fmsup_fmsub {c |}{col 34}{res}{space 2} .1494132{col 46}{space 2} .0432067{col 57}{space 1}    3.46{col 66}{space 3}0.001{col 74}{space 4} .0637128{col 87}{space 3} .2351135
{txt}{space 32} {c |}
{space 23}women_het {c |}
{space 30}1  {c |}{col 34}{res}{space 2}-.0261712{col 46}{space 2} .0086543{col 57}{space 1}   -3.02{col 66}{space 3}0.003{col 74}{space 4}-.0433369{col 87}{space 3}-.0090056
{txt}{space 30}2  {c |}{col 34}{res}{space 2}-.0561868{col 46}{space 2} .0075073{col 57}{space 1}   -7.48{col 66}{space 3}0.000{col 74}{space 4}-.0710775{col 87}{space 3}-.0412962
{txt}{space 32} {c |}
women_het#c.ln_ratio_fmsup_fmsub {c |}
{space 30}1  {c |}{col 34}{res}{space 2} .0005817{col 46}{space 2} .0225256{col 57}{space 1}    0.03{col 66}{space 3}0.979{col 74}{space 4}-.0440977{col 87}{space 3} .0452611
{txt}{space 30}2  {c |}{col 34}{res}{space 2} .0198587{col 46}{space 2} .0232969{col 57}{space 1}    0.85{col 66}{space 3}0.396{col 74}{space 4}-.0263507{col 87}{space 3}  .066068
{txt}{space 32} {c |}
{space 8}ln_ratio_fem_tot_men_tot {c |}{col 34}{res}{space 2}-.0073524{col 46}{space 2} .0558149{col 57}{space 1}   -0.13{col 66}{space 3}0.895{col 74}{space 4}-.1180609{col 87}{space 3} .1033562
{txt}{space 24}minority {c |}{col 34}{res}{space 2}-.0767962{col 46}{space 2} .0036644{col 57}{space 1}  -20.96{col 66}{space 3}0.000{col 74}{space 4}-.0840644{col 87}{space 3} -.069528
{txt}{space 22}supervisor {c |}{col 34}{res}{space 2} .1269454{col 46}{space 2} .0075184{col 57}{space 1}   16.88{col 66}{space 3}0.000{col 74}{space 4} .1120327{col 87}{space 3}  .141858
{txt}{space 18}topoffgender_2 {c |}{col 34}{res}{space 2}-.0036794{col 46}{space 2} .0046098{col 57}{space 1}   -0.80{col 66}{space 3}0.427{col 74}{space 4}-.0128229{col 87}{space 3} .0054641
{txt}{space 12}lntotworkforce_count {c |}{col 34}{res}{space 2} .0812766{col 46}{space 2} .0341665{col 57}{space 1}    2.38{col 66}{space 3}0.019{col 74}{space 4} .0135076{col 87}{space 3} .1490456
{txt}{space 4}ln_professionals_total_ratio {c |}{col 34}{res}{space 2} .0067216{col 46}{space 2} .0417928{col 57}{space 1}    0.16{col 66}{space 3}0.873{col 74}{space 4}-.0761743{col 87}{space 3} .0896174
{txt}{space 32} {c |}
{space 24}agencyid {c |}
{space 30}2  {c |}{col 34}{res}{space 2} .3257829{col 46}{space 2}  .121702{col 57}{space 1}    2.68{col 66}{space 3}0.009{col 74}{space 4} .0843876{col 87}{space 3} .5671783
{txt}{space 30}3  {c |}{col 34}{res}{space 2} .0987113{col 46}{space 2} .0574329{col 57}{space 1}    1.72{col 66}{space 3}0.089{col 74}{space 4}-.0152066{col 87}{space 3} .2126291
{txt}{space 30}4  {c |}{col 34}{res}{space 2} .4535847{col 46}{space 2} .1551889{col 57}{space 1}    2.92{col 66}{space 3}0.004{col 74}{space 4} .1457684{col 87}{space 3} .7614011
{txt}{space 30}5  {c |}{col 34}{res}{space 2} .2804522{col 46}{space 2}  .109987{col 57}{space 1}    2.55{col 66}{space 3}0.012{col 74}{space 4} .0622935{col 87}{space 3} .4986108
{txt}{space 30}6  {c |}{col 34}{res}{space 2} .2664864{col 46}{space 2} .1169074{col 57}{space 1}    2.28{col 66}{space 3}0.025{col 74}{space 4} .0346012{col 87}{space 3} .4983717
{txt}{space 30}7  {c |}{col 34}{res}{space 2} .3872192{col 46}{space 2}  .191802{col 57}{space 1}    2.02{col 66}{space 3}0.046{col 74}{space 4} .0067807{col 87}{space 3} .7676576
{txt}{space 30}8  {c |}{col 34}{res}{space 2} .3258011{col 46}{space 2} .1506225{col 57}{space 1}    2.16{col 66}{space 3}0.033{col 74}{space 4} .0270421{col 87}{space 3} .6245602
{txt}{space 30}9  {c |}{col 34}{res}{space 2}-.0144601{col 46}{space 2} .0231699{col 57}{space 1}   -0.62{col 66}{space 3}0.534{col 74}{space 4}-.0604175{col 87}{space 3} .0314974
{txt}{space 29}10  {c |}{col 34}{res}{space 2} .2762032{col 46}{space 2} .1673096{col 57}{space 1}    1.65{col 66}{space 3}0.102{col 74}{space 4}-.0556546{col 87}{space 3}  .608061
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{txt}{space 27}2018  {c |}{col 34}{res}{space 2}-.0330594{col 46}{space 2} .0074268{col 57}{space 1}   -4.45{col 66}{space 3}0.000{col 74}{space 4}-.0477904{col 87}{space 3}-.0183283
{txt}{space 27}2019  {c |}{col 34}{res}{space 2}-.0723914{col 46}{space 2} .0085239{col 57}{space 1}   -8.49{col 66}{space 3}0.000{col 74}{space 4}-.0892985{col 87}{space 3}-.0554842
{txt}{space 32} {c |}
{space 27}_cons {c |}{col 34}{res}{space 2}-.2057905{col 46}{space 2}  .439522{col 57}{space 1}   -0.47{col 66}{space 3}0.641{col 74}{space 4} -1.07758{col 87}{space 3} .6659992
{txt}{hline 33}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16} 2,463,525{col 28} -1909279{col 39} -1858542{col 50}    20{col 58}  3717125{col 69}  3717379
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] IC note}}.{p_end}

{com}. *
. ** BETWEEN-IDENTITY "IN" GROUP VERSUS "OUT" GROUP STATUS DIFFERENTIAL BETWEEN GENDERED RESPONDENTS **
. 
. lincom c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .1494132{col 26}{space 2} .0432067{col 37}{space 1}    3.46{col 46}{space 3}0.001{col 54}{space 4} .0637128{col 67}{space 3} .2351135
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  1.women_het#c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.women_het#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0005817{col 26}{space 2} .0225256{col 37}{space 1}    0.03{col 46}{space 3}0.979{col 54}{space 4}-.0440977{col 67}{space 3} .0452611
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  2.women_het#c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}2.women_het#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0198587{col 26}{space 2} .0232969{col 37}{space 1}    0.85{col 46}{space 3}0.396{col 54}{space 4}-.0263507{col 67}{space 3}  .066068
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  2.women_het#c.ln_ratio_fmsup_fmsub -  1.women_het#c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1}{space 1}{res}- 1.women_het#c.ln_ratio_fmsup_fmsub + 2.women_het#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}  .019277{col 26}{space 2} .0196536{col 37}{space 1}    0.98{col 46}{space 3}0.329{col 54}{space 4}-.0197059{col 67}{space 3} .0582599
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. *
. 
. 
.    
. *** MODEL G4.2: CONDITIONAL RESPONSES BY MINORITY WOMEN VERSUS MINORITY MEN [BASELINE CATEGORY: NON-MINORITY RESPONDENTS] -- RACIAL/ETHNIC BETWEEN-IDENTITY GROUP STATUS DIFFERENTIAL MODEL: [MINORITY SUPERVISORS WITHIN AGENCY j IN YEAR t / NON-MINORITY SUPERVISORS WITHIN AGENCY j IN YEAR t] / [MINORITY NON-SUPERVISORS WITHIN AGENCY j IN YEAR t / NON-MINORITY NON-SUPERVISORS WITHIN AGENCY j IN YEAR t] -- CONTROLLING FOR RACIAL/ETHNIC SUPERVISORY EMPLOYEE IDENTITY GROUP DIFFERENTIAL ***
. 
. regress  lndiversity2zeroadj  c.ln_ratio_mnmsup_mnmsub##i.minority_het   ln_ratio_min_tot_nmin_tot   gender supervisor  topoffminority_2 lntotworkforce_count  ln_professionals_total_ratio   i.agencyid i.year if e(sample) & ln_ratio_mnmsup_mnmsub<=0, vce(cluster agencyid)

{txt}Linear regression                               Number of obs     = {res} 2,412,654
                                                {txt}{help j_robustsingular:F(19, 99) }        =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0404
                                                {txt}Root MSE          =    {res}  .5166

{txt}{ralign 103:(Std. err. adjusted for {res:100} clusters in {res:agencyid})}
{hline 38}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 39}{c |}{col 51}    Robust
{col 1}                  lndiversity2zeroadj{col 39}{c |} Coefficient{col 51}  std. err.{col 63}      t{col 71}   P>|t|{col 79}     [95% con{col 92}f. interval]
{hline 38}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}ln_ratio_mnmsup_mnmsub {c |}{col 39}{res}{space 2} .0489245{col 51}{space 2} .0361882{col 62}{space 1}    1.35{col 71}{space 3}0.179{col 79}{space 4}-.0228808{col 92}{space 3} .1207297
{txt}{space 37} {c |}
{space 25}minority_het {c |}
{space 35}1  {c |}{col 39}{res}{space 2}-.0648849{col 51}{space 2} .0065405{col 62}{space 1}   -9.92{col 71}{space 3}0.000{col 79}{space 4}-.0778626{col 92}{space 3}-.0519072
{txt}{space 35}2  {c |}{col 39}{res}{space 2}-.0913713{col 51}{space 2} .0080491{col 62}{space 1}  -11.35{col 71}{space 3}0.000{col 79}{space 4}-.1073425{col 92}{space 3}-.0754001
{txt}{space 37} {c |}
minority_het#c.ln_ratio_mnmsup_mnmsub {c |}
{space 35}1  {c |}{col 39}{res}{space 2} .0341389{col 51}{space 2} .0175879{col 62}{space 1}    1.94{col 71}{space 3}0.055{col 79}{space 4}-.0007594{col 92}{space 3} .0690372
{txt}{space 35}2  {c |}{col 39}{res}{space 2} .0610548{col 51}{space 2} .0212944{col 62}{space 1}    2.87{col 71}{space 3}0.005{col 79}{space 4} .0188022{col 92}{space 3} .1033074
{txt}{space 37} {c |}
{space 12}ln_ratio_min_tot_nmin_tot {c |}{col 39}{res}{space 2} .0576034{col 51}{space 2} .0482185{col 62}{space 1}    1.19{col 71}{space 3}0.235{col 79}{space 4}-.0380726{col 92}{space 3} .1532793
{txt}{space 31}gender {c |}{col 39}{res}{space 2} -.026678{col 51}{space 2} .0046975{col 62}{space 1}   -5.68{col 71}{space 3}0.000{col 79}{space 4} -.035999{col 92}{space 3}-.0173571
{txt}{space 27}supervisor {c |}{col 39}{res}{space 2} .1282862{col 51}{space 2} .0077209{col 62}{space 1}   16.62{col 71}{space 3}0.000{col 79}{space 4} .1129662{col 92}{space 3} .1436062
{txt}{space 21}topoffminority_2 {c |}{col 39}{res}{space 2}  .010242{col 51}{space 2} .0055094{col 62}{space 1}    1.86{col 71}{space 3}0.066{col 79}{space 4}-.0006897{col 92}{space 3} .0211738
{txt}{space 17}lntotworkforce_count {c |}{col 39}{res}{space 2} .0653933{col 51}{space 2} .0445955{col 62}{space 1}    1.47{col 71}{space 3}0.146{col 79}{space 4}-.0230938{col 92}{space 3} .1538805
{txt}{space 9}ln_professionals_total_ratio {c |}{col 39}{res}{space 2}  .018931{col 51}{space 2} .0525641{col 62}{space 1}    0.36{col 71}{space 3}0.720{col 79}{space 4}-.0853677{col 92}{space 3} .1232296
{txt}{space 37} {c |}
{space 29}agencyid {c |}
{space 35}2  {c |}{col 39}{res}{space 2} .2148759{col 51}{space 2}  .136271{col 62}{space 1}    1.58{col 71}{space 3}0.118{col 79}{space 4}-.0555153{col 92}{space 3}  .485267
{txt}{space 35}4  {c |}{col 39}{res}{space 2} .2796058{col 51}{space 2} .1770839{col 62}{space 1}    1.58{col 71}{space 3}0.118{col 79}{space 4}-.0717672{col 92}{space 3} .6309787
{txt}{space 35}5  {c |}{col 39}{res}{space 2} .1639413{col 51}{space 2}  .140983{col 62}{space 1}    1.16{col 71}{space 3}0.248{col 79}{space 4}-.1157996{col 92}{space 3} .4436823
{txt}{space 35}7  {c |}{col 39}{res}{space 2} .2427707{col 51}{space 2} .2401101{col 62}{space 1}    1.01{col 71}{space 3}0.314{col 79}{space 4}-.2336598{col 92}{space 3} .7192013
{txt}{space 35}8  {c |}{col 39}{res}{space 2} .2312116{col 51}{space 2} .1793999{col 62}{space 1}    1.29{col 71}{space 3}0.200{col 79}{space 4}-.1247567{col 92}{space 3}   .58718
{txt}{space 35}9  {c |}{col 39}{res}{space 2}-.0565106{col 51}{space 2}  .023939{col 62}{space 1}   -2.36{col 71}{space 3}0.020{col 79}{space 4}-.1040108{col 92}{space 3}-.0090104
{txt}{space 34}10  {c |}{col 39}{res}{space 2} .1826007{col 51}{space 2} .2527361{col 62}{space 1}    0.72{col 71}{space 3}0.472{col 79}{space 4}-.3188825{col 92}{space 3} .6840838
{txt}{space 34}11  {c |}{col 39}{res}{space 2} .1666337{col 51}{space 2} .1179078{col 62}{space 1}    1.41{col 71}{space 3}0.161{col 79}{space 4}-.0673209{col 92}{space 3} .4005882
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{txt}{space 34}21  {c |}{col 39}{res}{space 2} .2126781{col 51}{space 2} .1227505{col 62}{space 1}    1.73{col 71}{space 3}0.086{col 79}{space 4}-.0308854{col 92}{space 3} .4562417
{txt}{space 34}22  {c |}{col 39}{res}{space 2} .1459989{col 51}{space 2} .1921929{col 62}{space 1}    0.76{col 71}{space 3}0.449{col 79}{space 4}-.2353535{col 92}{space 3} .5273513
{txt}{space 34}23  {c |}{col 39}{res}{space 2}-.1108589{col 51}{space 2} .0970296{col 62}{space 1}   -1.14{col 71}{space 3}0.256{col 79}{space 4}-.3033866{col 92}{space 3} .0816688
{txt}{space 34}24  {c |}{col 39}{res}{space 2} .2488842{col 51}{space 2}  .135314{col 62}{space 1}    1.84{col 71}{space 3}0.069{col 79}{space 4}-.0196081{col 92}{space 3} .5173765
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{txt}{space 34}26  {c |}{col 39}{res}{space 2} .0460985{col 51}{space 2}  .142612{col 62}{space 1}    0.32{col 71}{space 3}0.747{col 79}{space 4}-.2368747{col 92}{space 3} .3290717
{txt}{space 34}27  {c |}{col 39}{res}{space 2} .3601496{col 51}{space 2} .2170858{col 62}{space 1}    1.66{col 71}{space 3}0.100{col 79}{space 4}-.0705958{col 92}{space 3}  .790895
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{txt}{space 34}32  {c |}{col 39}{res}{space 2} .2049186{col 51}{space 2} .1581026{col 62}{space 1}    1.30{col 71}{space 3}0.198{col 79}{space 4}-.1087913{col 92}{space 3} .5186285
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{txt}{space 34}37  {c |}{col 39}{res}{space 2} .2034287{col 51}{space 2} .1277291{col 62}{space 1}    1.59{col 71}{space 3}0.114{col 79}{space 4}-.0500136{col 92}{space 3}  .456871
{txt}{space 34}38  {c |}{col 39}{res}{space 2} .3131225{col 51}{space 2} .2172772{col 62}{space 1}    1.44{col 71}{space 3}0.153{col 79}{space 4}-.1180027{col 92}{space 3} .7442476
{txt}{space 34}39  {c |}{col 39}{res}{space 2} .2235992{col 51}{space 2} .1302792{col 62}{space 1}    1.72{col 71}{space 3}0.089{col 79}{space 4} -.034903{col 92}{space 3} .4821015
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{txt}{space 34}61  {c |}{col 39}{res}{space 2} .1380243{col 51}{space 2} .1211955{col 62}{space 1}    1.14{col 71}{space 3}0.258{col 79}{space 4}-.1024539{col 92}{space 3} .3785024
{txt}{space 34}62  {c |}{col 39}{res}{space 2} .3218658{col 51}{space 2} .2262218{col 62}{space 1}    1.42{col 71}{space 3}0.158{col 79}{space 4}-.1270074{col 92}{space 3} .7707389
{txt}{space 34}63  {c |}{col 39}{res}{space 2}  .390706{col 51}{space 2} .2233381{col 62}{space 1}    1.75{col 71}{space 3}0.083{col 79}{space 4}-.0524454{col 92}{space 3} .8338573
{txt}{space 34}64  {c |}{col 39}{res}{space 2} .4050119{col 51}{space 2} .2329253{col 62}{space 1}    1.74{col 71}{space 3}0.085{col 79}{space 4}-.0571624{col 92}{space 3} .8671862
{txt}{space 34}65  {c |}{col 39}{res}{space 2} .2110143{col 51}{space 2} .1343051{col 62}{space 1}    1.57{col 71}{space 3}0.119{col 79}{space 4}-.0554761{col 92}{space 3} .4775047
{txt}{space 34}66  {c |}{col 39}{res}{space 2} .1930809{col 51}{space 2}  .247282{col 62}{space 1}    0.78{col 71}{space 3}0.437{col 79}{space 4}-.2975802{col 92}{space 3}  .683742
{txt}{space 34}67  {c |}{col 39}{res}{space 2} .2231392{col 51}{space 2} .1481923{col 62}{space 1}    1.51{col 71}{space 3}0.135{col 79}{space 4}-.0709065{col 92}{space 3} .5171849
{txt}{space 34}68  {c |}{col 39}{res}{space 2} .2780389{col 51}{space 2} .1677445{col 62}{space 1}    1.66{col 71}{space 3}0.101{col 79}{space 4}-.0548025{col 92}{space 3} .6108803
{txt}{space 34}69  {c |}{col 39}{res}{space 2} .1424688{col 51}{space 2} .1337927{col 62}{space 1}    1.06{col 71}{space 3}0.290{col 79}{space 4} -.123005{col 92}{space 3} .4079427
{txt}{space 34}70  {c |}{col 39}{res}{space 2} .3453331{col 51}{space 2} .2322794{col 62}{space 1}    1.49{col 71}{space 3}0.140{col 79}{space 4}-.1155597{col 92}{space 3} .8062259
{txt}{space 34}71  {c |}{col 39}{res}{space 2}-.1146932{col 51}{space 2} .1995893{col 62}{space 1}   -0.57{col 71}{space 3}0.567{col 79}{space 4}-.5107217{col 92}{space 3} .2813354
{txt}{space 34}72  {c |}{col 39}{res}{space 2} .2007952{col 51}{space 2} .1278341{col 62}{space 1}    1.57{col 71}{space 3}0.119{col 79}{space 4}-.0528555{col 92}{space 3} .4544459
{txt}{space 34}73  {c |}{col 39}{res}{space 2} .4285546{col 51}{space 2} .2124928{col 62}{space 1}    2.02{col 71}{space 3}0.046{col 79}{space 4} .0069228{col 92}{space 3} .8501865
{txt}{space 34}75  {c |}{col 39}{res}{space 2} .0629717{col 51}{space 2} .1669839{col 62}{space 1}    0.38{col 71}{space 3}0.707{col 79}{space 4}-.2683606{col 92}{space 3} .3943041
{txt}{space 34}76  {c |}{col 39}{res}{space 2} .2995255{col 51}{space 2} .1909686{col 62}{space 1}    1.57{col 71}{space 3}0.120{col 79}{space 4}-.0793976{col 92}{space 3} .6784485
{txt}{space 34}77  {c |}{col 39}{res}{space 2} .2199409{col 51}{space 2} .1886948{col 62}{space 1}    1.17{col 71}{space 3}0.247{col 79}{space 4}-.1544705{col 92}{space 3} .5943523
{txt}{space 34}78  {c |}{col 39}{res}{space 2}  .265421{col 51}{space 2} .1201774{col 62}{space 1}    2.21{col 71}{space 3}0.030{col 79}{space 4}  .026963{col 92}{space 3} .5038791
{txt}{space 34}79  {c |}{col 39}{res}{space 2} -.017061{col 51}{space 2} .0259246{col 62}{space 1}   -0.66{col 71}{space 3}0.512{col 79}{space 4} -.068501{col 92}{space 3}  .034379
{txt}{space 34}80  {c |}{col 39}{res}{space 2} .4318665{col 51}{space 2} .2376731{col 62}{space 1}    1.82{col 71}{space 3}0.072{col 79}{space 4}-.0397285{col 92}{space 3} .9034616
{txt}{space 34}81  {c |}{col 39}{res}{space 2} .1534747{col 51}{space 2} .2635274{col 62}{space 1}    0.58{col 71}{space 3}0.562{col 79}{space 4}-.3694209{col 92}{space 3} .6763703
{txt}{space 34}82  {c |}{col 39}{res}{space 2} .2359132{col 51}{space 2}  .225252{col 62}{space 1}    1.05{col 71}{space 3}0.297{col 79}{space 4}-.2110357{col 92}{space 3}  .682862
{txt}{space 34}83  {c |}{col 39}{res}{space 2} .3476444{col 51}{space 2} .1859877{col 62}{space 1}    1.87{col 71}{space 3}0.065{col 79}{space 4}-.0213956{col 92}{space 3} .7166843
{txt}{space 34}84  {c |}{col 39}{res}{space 2}  .269261{col 51}{space 2}  .231827{col 62}{space 1}    1.16{col 71}{space 3}0.248{col 79}{space 4} -.190734{col 92}{space 3}  .729256
{txt}{space 34}85  {c |}{col 39}{res}{space 2} .3011649{col 51}{space 2} .1778633{col 62}{space 1}    1.69{col 71}{space 3}0.094{col 79}{space 4}-.0517544{col 92}{space 3} .6540842
{txt}{space 34}86  {c |}{col 39}{res}{space 2} .3282404{col 51}{space 2} .2653442{col 62}{space 1}    1.24{col 71}{space 3}0.219{col 79}{space 4}  -.19826{col 92}{space 3} .8547408
{txt}{space 34}87  {c |}{col 39}{res}{space 2} .3796492{col 51}{space 2}  .263184{col 62}{space 1}    1.44{col 71}{space 3}0.152{col 79}{space 4} -.142565{col 92}{space 3} .9018634
{txt}{space 34}88  {c |}{col 39}{res}{space 2} .1655523{col 51}{space 2} .1865714{col 62}{space 1}    0.89{col 71}{space 3}0.377{col 79}{space 4}-.2046458{col 92}{space 3} .5357504
{txt}{space 34}89  {c |}{col 39}{res}{space 2} .2328907{col 51}{space 2} .1802647{col 62}{space 1}    1.29{col 71}{space 3}0.199{col 79}{space 4}-.1247936{col 92}{space 3} .5905751
{txt}{space 34}90  {c |}{col 39}{res}{space 2} .0145181{col 51}{space 2} .0932994{col 62}{space 1}    0.16{col 71}{space 3}0.877{col 79}{space 4}-.1706081{col 92}{space 3} .1996444
{txt}{space 34}91  {c |}{col 39}{res}{space 2} .1414367{col 51}{space 2} .1306486{col 62}{space 1}    1.08{col 71}{space 3}0.282{col 79}{space 4}-.1177984{col 92}{space 3} .4006719
{txt}{space 34}92  {c |}{col 39}{res}{space 2} .0802244{col 51}{space 2} .0662076{col 62}{space 1}    1.21{col 71}{space 3}0.229{col 79}{space 4}-.0511458{col 92}{space 3} .2115947
{txt}{space 34}93  {c |}{col 39}{res}{space 2} .3563406{col 51}{space 2} .1916515{col 62}{space 1}    1.86{col 71}{space 3}0.066{col 79}{space 4}-.0239375{col 92}{space 3} .7366187
{txt}{space 34}94  {c |}{col 39}{res}{space 2} .4534127{col 51}{space 2} .2348804{col 62}{space 1}    1.93{col 71}{space 3}0.056{col 79}{space 4} -.012641{col 92}{space 3} .9194663
{txt}{space 34}95  {c |}{col 39}{res}{space 2} .1539471{col 51}{space 2} .2336804{col 62}{space 1}    0.66{col 71}{space 3}0.512{col 79}{space 4}-.3097254{col 92}{space 3} .6176197
{txt}{space 34}96  {c |}{col 39}{res}{space 2} .2949666{col 51}{space 2} .2066584{col 62}{space 1}    1.43{col 71}{space 3}0.157{col 79}{space 4}-.1150886{col 92}{space 3} .7050217
{txt}{space 34}97  {c |}{col 39}{res}{space 2} .3339819{col 51}{space 2} .1715901{col 62}{space 1}    1.95{col 71}{space 3}0.054{col 79}{space 4}-.0064901{col 92}{space 3} .6744538
{txt}{space 34}98  {c |}{col 39}{res}{space 2} .4428242{col 51}{space 2} .2456801{col 62}{space 1}    1.80{col 71}{space 3}0.075{col 79}{space 4}-.0446584{col 92}{space 3} .9303068
{txt}{space 34}99  {c |}{col 39}{res}{space 2} .0418126{col 51}{space 2} .0620291{col 62}{space 1}    0.67{col 71}{space 3}0.502{col 79}{space 4}-.0812665{col 92}{space 3} .1648917
{txt}{space 33}100  {c |}{col 39}{res}{space 2} .2044484{col 51}{space 2}   .23259{col 62}{space 1}    0.88{col 71}{space 3}0.382{col 79}{space 4}-.2570606{col 92}{space 3} .6659575
{txt}{space 33}101  {c |}{col 39}{res}{space 2} .3658445{col 51}{space 2} .1833457{col 62}{space 1}    2.00{col 71}{space 3}0.049{col 79}{space 4} .0020468{col 92}{space 3} .7296422
{txt}{space 33}103  {c |}{col 39}{res}{space 2} .0952371{col 51}{space 2} .1306626{col 62}{space 1}    0.73{col 71}{space 3}0.468{col 79}{space 4}-.1640258{col 92}{space 3}    .3545
{txt}{space 33}104  {c |}{col 39}{res}{space 2}-.1601813{col 51}{space 2}  .061684{col 62}{space 1}   -2.60{col 71}{space 3}0.011{col 79}{space 4}-.2825757{col 92}{space 3}-.0377868
{txt}{space 33}105  {c |}{col 39}{res}{space 2}  .226567{col 51}{space 2} .2207675{col 62}{space 1}    1.03{col 71}{space 3}0.307{col 79}{space 4}-.2114835{col 92}{space 3} .6646175
{txt}{space 37} {c |}
{space 33}year {c |}
{space 32}2011  {c |}{col 39}{res}{space 2}-.0018207{col 51}{space 2}  .003904{col 62}{space 1}   -0.47{col 71}{space 3}0.642{col 79}{space 4}-.0095671{col 92}{space 3} .0059256
{txt}{space 32}2012  {c |}{col 39}{res}{space 2} .0053126{col 51}{space 2} .0047204{col 62}{space 1}    1.13{col 71}{space 3}0.263{col 79}{space 4}-.0040537{col 92}{space 3} .0146789
{txt}{space 32}2013  {c |}{col 39}{res}{space 2} .0026091{col 51}{space 2} .0058867{col 62}{space 1}    0.44{col 71}{space 3}0.659{col 79}{space 4}-.0090714{col 92}{space 3} .0142897
{txt}{space 32}2014  {c |}{col 39}{res}{space 2}-.0024886{col 51}{space 2} .0068837{col 62}{space 1}   -0.36{col 71}{space 3}0.718{col 79}{space 4}-.0161474{col 92}{space 3} .0111701
{txt}{space 32}2015  {c |}{col 39}{res}{space 2}-.0099687{col 51}{space 2} .0084119{col 62}{space 1}   -1.19{col 71}{space 3}0.239{col 79}{space 4}-.0266597{col 92}{space 3} .0067224
{txt}{space 32}2016  {c |}{col 39}{res}{space 2}-.0132299{col 51}{space 2}   .00847{col 62}{space 1}   -1.56{col 71}{space 3}0.121{col 79}{space 4}-.0300362{col 92}{space 3} .0035765
{txt}{space 32}2017  {c |}{col 39}{res}{space 2} -.002728{col 51}{space 2} .0124457{col 62}{space 1}   -0.22{col 71}{space 3}0.827{col 79}{space 4}-.0274229{col 92}{space 3}  .021967
{txt}{space 32}2018  {c |}{col 39}{res}{space 2}-.0291077{col 51}{space 2} .0113052{col 62}{space 1}   -2.57{col 71}{space 3}0.012{col 79}{space 4}-.0515396{col 92}{space 3}-.0066759
{txt}{space 32}2019  {c |}{col 39}{res}{space 2}  -.07005{col 51}{space 2} .0126385{col 62}{space 1}   -5.54{col 71}{space 3}0.000{col 79}{space 4}-.0951276{col 92}{space 3}-.0449725
{txt}{space 37} {c |}
{space 32}_cons {c |}{col 39}{res}{space 2} .0312285{col 51}{space 2} .5639634{col 62}{space 1}    0.06{col 71}{space 3}0.956{col 79}{space 4}-1.087797{col 92}{space 3} 1.150254
{txt}{hline 38}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16} 2,412,654{col 28} -1879502{col 39} -1829805{col 50}    20{col 58}  3659651{col 69}  3659904
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] IC note}}.{p_end}

{com}. *
. ** BETWEEN-IDENTITY "IN" GROUP VERSUS "OUT" GROUP STATUS DIFFERENTIAL BETWEEN MINORITY/NON-MINORITY RESPONDENTS **
. 
. lincom c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0489245{col 26}{space 2} .0361882{col 37}{space 1}    1.35{col 46}{space 3}0.179{col 54}{space 4}-.0228808{col 67}{space 3} .1207297
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  1.minority_het#c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.minority_het#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0341389{col 26}{space 2} .0175879{col 37}{space 1}    1.94{col 46}{space 3}0.055{col 54}{space 4}-.0007594{col 67}{space 3} .0690372
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  2.minority_het#c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}2.minority_het#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0610548{col 26}{space 2} .0212944{col 37}{space 1}    2.87{col 46}{space 3}0.005{col 54}{space 4} .0188022{col 67}{space 3} .1033074
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  2.minority_het#c.ln_ratio_mnmsup_mnmsub -  1.minority_het#c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1}{space 1}{res}- 1.minority_het#c.ln_ratio_mnmsup_mnmsub + 2.minority_het#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0269159{col 26}{space 2} .0145568{col 37}{space 1}    1.85{col 46}{space 3}0.067{col 54}{space 4}-.0019679{col 67}{space 3} .0557996
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. *
. 
. **********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
.    
. *** 5. CONDITIONAL-RESPONDENT MODELS EVALUATING THE RELATIONSHIP INVOLVING WITHIN-IDENTITY "OUT-GROUP" STATUS & BETWEEN-IDENTITY GROUP STATUS DIFFERENTIALS AS A MEANS TO FOSTER DIVERSITY AND INCLUSION IN THE U.S. CIVILIAN WORKFORCE  [BY NON-SUPERVISORS POSITIONS VERSUS SUPERVISORY POSITION] ***   
. 
. 
.    
. *** MODEL G5.2: CONDITIONAL RESPONSES BY GENDER & POSITION --  GENDER WITHIN-IDENTITY 'OUT-GROUP' STATUS DIFFERENTIAL MODEL: [WOMEN SUPERVISORS WITHIN AGENCY j IN YEAR t / WOMEN NON-SUPERVISORS WITHIN AGENCY j IN YEAR t] -- CONTROLLING FOR GENDER SUPERVISORY EMPLOYEE IDENTITY GROUP DIFFERENTIAL ***
. 
. regress  lndiversity2zeroadj  c.ln_ratio_fmsup_fmsub##i.gender##i.supervisor   ln_ratio_fem_tot_men_tot   minority  topoffgender_2 lntotworkforce_count  ln_professionals_total_ratio   i.agencyid i.year if ln_ratio_fmsup_fmsub <= 0, vce(cluster agencyid)

{txt}Linear regression                               Number of obs     = {res} 2,463,525
                                                {txt}{help j_robustsingular:F(20, 102) }       =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0402
                                                {txt}Root MSE          =    {res} .51459

{txt}{ralign 106:(Std. err. adjusted for {res:103} clusters in {res:agencyid})}
{hline 41}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 42}{c |}{col 54}    Robust
{col 1}                     lndiversity2zeroadj{col 42}{c |} Coefficient{col 54}  std. err.{col 66}      t{col 74}   P>|t|{col 82}     [95% con{col 95}f. interval]
{hline 41}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}ln_ratio_fmsup_fmsub {c |}{col 42}{res}{space 2} .1329165{col 54}{space 2}  .046552{col 65}{space 1}    2.86{col 74}{space 3}0.005{col 82}{space 4} .0405808{col 95}{space 3} .2252522
{txt}{space 32}1.gender {c |}{col 42}{res}{space 2}-.0358635{col 54}{space 2} .0108455{col 65}{space 1}   -3.31{col 74}{space 3}0.001{col 82}{space 4}-.0573754{col 95}{space 3}-.0143516
{txt}{space 40} {c |}
{space 11}gender#c.ln_ratio_fmsup_fmsub {c |}
{space 38}1  {c |}{col 42}{res}{space 2}  .014757{col 54}{space 2} .0280736{col 65}{space 1}    0.53{col 74}{space 3}0.600{col 82}{space 4}-.0409269{col 95}{space 3} .0704409
{txt}{space 40} {c |}
{space 28}1.supervisor {c |}{col 42}{res}{space 2} .1467705{col 54}{space 2} .0227619{col 65}{space 1}    6.45{col 74}{space 3}0.000{col 82}{space 4} .1016225{col 95}{space 3} .1919186
{txt}{space 40} {c |}
{space 7}supervisor#c.ln_ratio_fmsup_fmsub {c |}
{space 38}1  {c |}{col 42}{res}{space 2} .0625821{col 54}{space 2} .0457603{col 65}{space 1}    1.37{col 74}{space 3}0.174{col 82}{space 4}-.0281831{col 95}{space 3} .1533474
{txt}{space 40} {c |}
{space 23}gender#supervisor {c |}
{space 36}1 1  {c |}{col 42}{res}{space 2} .0031501{col 54}{space 2} .0158993{col 65}{space 1}    0.20{col 74}{space 3}0.843{col 82}{space 4}-.0283861{col 95}{space 3} .0346863
{txt}{space 40} {c |}
gender#supervisor#c.ln_ratio_fmsup_fmsub {c |}
{space 36}1 1  {c |}{col 42}{res}{space 2} -.012012{col 54}{space 2} .0324699{col 65}{space 1}   -0.37{col 74}{space 3}0.712{col 82}{space 4}-.0764159{col 95}{space 3} .0523919
{txt}{space 40} {c |}
{space 16}ln_ratio_fem_tot_men_tot {c |}{col 42}{res}{space 2}-.0084082{col 54}{space 2} .0557203{col 65}{space 1}   -0.15{col 74}{space 3}0.880{col 82}{space 4}-.1189292{col 95}{space 3} .1021127
{txt}{space 32}minority {c |}{col 42}{res}{space 2}-.0943735{col 54}{space 2} .0038261{col 65}{space 1}  -24.67{col 74}{space 3}0.000{col 82}{space 4}-.1019626{col 95}{space 3}-.0867844
{txt}{space 26}topoffgender_2 {c |}{col 42}{res}{space 2}-.0036132{col 54}{space 2} .0045968{col 65}{space 1}   -0.79{col 74}{space 3}0.434{col 82}{space 4} -.012731{col 95}{space 3} .0055047
{txt}{space 20}lntotworkforce_count {c |}{col 42}{res}{space 2} .0827202{col 54}{space 2}    .0339{col 65}{space 1}    2.44{col 74}{space 3}0.016{col 82}{space 4} .0154796{col 95}{space 3} .1499607
{txt}{space 12}ln_professionals_total_ratio {c |}{col 42}{res}{space 2} .0064134{col 54}{space 2} .0414207{col 65}{space 1}    0.15{col 74}{space 3}0.877{col 82}{space 4}-.0757444{col 95}{space 3} .0885712
{txt}{space 40} {c |}
{space 32}agencyid {c |}
{space 38}2  {c |}{col 42}{res}{space 2} .3367974{col 54}{space 2} .1207292{col 65}{space 1}    2.79{col 74}{space 3}0.006{col 82}{space 4} .0973315{col 95}{space 3} .5762632
{txt}{space 38}3  {c |}{col 42}{res}{space 2} .1047358{col 54}{space 2}  .056964{col 65}{space 1}    1.84{col 74}{space 3}0.069{col 82}{space 4}-.0082521{col 95}{space 3} .2177237
{txt}{space 38}4  {c |}{col 42}{res}{space 2}  .468108{col 54}{space 2} .1533691{col 65}{space 1}    3.05{col 74}{space 3}0.003{col 82}{space 4} .1639012{col 95}{space 3} .7723148
{txt}{space 38}5  {c |}{col 42}{res}{space 2} .2860828{col 54}{space 2} .1090993{col 65}{space 1}    2.62{col 74}{space 3}0.010{col 82}{space 4} .0696847{col 95}{space 3} .5024808
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{txt}{space 35}2014  {c |}{col 42}{res}{space 2}-.0074951{col 54}{space 2} .0071971{col 65}{space 1}   -1.04{col 74}{space 3}0.300{col 82}{space 4}-.0217705{col 95}{space 3} .0067804
{txt}{space 35}2015  {c |}{col 42}{res}{space 2} -.014495{col 54}{space 2} .0088019{col 65}{space 1}   -1.65{col 74}{space 3}0.103{col 82}{space 4}-.0319534{col 95}{space 3} .0029635
{txt}{space 35}2016  {c |}{col 42}{res}{space 2} -.018293{col 54}{space 2} .0076463{col 65}{space 1}   -2.39{col 74}{space 3}0.019{col 82}{space 4}-.0334595{col 95}{space 3}-.0031265
{txt}{space 35}2017  {c |}{col 42}{res}{space 2}-.0036554{col 54}{space 2} .0098513{col 65}{space 1}   -0.37{col 74}{space 3}0.711{col 82}{space 4}-.0231954{col 95}{space 3} .0158846
{txt}{space 35}2018  {c |}{col 42}{res}{space 2}-.0331675{col 54}{space 2} .0074693{col 65}{space 1}   -4.44{col 74}{space 3}0.000{col 82}{space 4} -.047983{col 95}{space 3}-.0183521
{txt}{space 35}2019  {c |}{col 42}{res}{space 2} -.072571{col 54}{space 2} .0085525{col 65}{space 1}   -8.49{col 74}{space 3}0.000{col 82}{space 4}-.0895348{col 95}{space 3}-.0556073
{txt}{space 40} {c |}
{space 35}_cons {c |}{col 42}{res}{space 2}-.2247814{col 54}{space 2} .4359716{col 65}{space 1}   -0.52{col 74}{space 3}0.607{col 82}{space 4}-1.089529{col 95}{space 3} .6399661
{txt}{hline 41}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16} 2,463,525{col 28} -1909279{col 39} -1858786{col 50}    21{col 58}  3717614{col 69}  3717881
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] IC note}}.{p_end}

{com}. *
. ** BY NON-SUPERVISORS RESPONDENT: WITHIN-IDENTITY "OUT" GROUP STATUS DIFFERENTIAL BETWEEN GENDERED RESPONDENTS **
. 
. lincom c.ln_ratio_fmsup_fmsub 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .1329165{col 26}{space 2}  .046552{col 37}{space 1}    2.86{col 46}{space 3}0.005{col 54}{space 4} .0405808{col 67}{space 3} .2252522
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  1.gender#c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.gender#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}  .014757{col 26}{space 2} .0280736{col 37}{space 1}    0.53{col 46}{space 3}0.600{col 54}{space 4}-.0409269{col 67}{space 3} .0704409
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *
. *
. *
. *
. 
. ** BY SUPERVISOR RESPONDENT: WITHIN-IDENTITY "OUT" GROUP STATUS DIFFERENTIAL BETWEEN GENDERED RESPONDENTS **
. 
. lincom c.ln_ratio_fmsup_fmsub + 1.supervisor#c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}ln_ratio_fmsup_fmsub + 1.supervisor#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .1954986{col 26}{space 2} .0508801{col 37}{space 1}    3.84{col 46}{space 3}0.000{col 54}{space 4} .0945782{col 67}{space 3} .2964191
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  1.gender#c.ln_ratio_fmsup_fmsub +  1.gender#1.supervisor#c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.gender#c.ln_ratio_fmsup_fmsub + 1.gender#1.supervisor#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}  .002745{col 26}{space 2} .0199877{col 37}{space 1}    0.14{col 46}{space 3}0.891{col 54}{space 4}-.0369005{col 67}{space 3} .0423905
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. 
. 
. 
. *** MODEL G6.2: CONDITIONAL RESPONSES BY RACE/ETHNICITIY & POSITION -- RACIAL/ETHNIC WITHIN-'OUT-GROUP' STATUS DIFFERENTIAL MODEL: [MINORITY SUPERVISORS WITHIN AGENCY j IN YEAR t / NON-MINORITY NON-SUPERVISORS WITHIN AGENCY j IN YEAR t]  -- CONTROLLING FOR RACIAL/ETHNIC SUPERVISORY EMPLOYEE IDENTITY GROUP DIFFERENTIAL ***
. 
. regress lndiversity2zeroadj  c.ln_ratio_mnmsup_mnmsub##i.minority##i.supervisor  ln_ratio_min_tot_nmin_tot   gender  topoffminority_2 lntotworkforce_count  ln_professionals_total_ratio  i.agencyid i.year if ln_ratio_mnmsup_mnmsub<=0, vce(cluster agencyid)

{txt}Linear regression                               Number of obs     = {res} 2,454,819
                                                {txt}{help j_robustsingular:F(20, 101) }       =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0405
                                                {txt}Root MSE          =    {res} .51625

{txt}{ralign 110:(Std. err. adjusted for {res:102} clusters in {res:agencyid})}
{hline 45}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 46}{c |}{col 58}    Robust
{col 1}                         lndiversity2zeroadj{col 46}{c |} Coefficient{col 58}  std. err.{col 70}      t{col 78}   P>|t|{col 86}     [95% con{col 99}f. interval]
{hline 45}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}ln_ratio_mnmsup_mnmsub {c |}{col 46}{res}{space 2} .0445514{col 58}{space 2} .0365177{col 69}{space 1}    1.22{col 78}{space 3}0.225{col 86}{space 4}-.0278899{col 99}{space 3} .1169926
{txt}{space 34}1.minority {c |}{col 46}{res}{space 2}-.0761652{col 58}{space 2} .0077046{col 69}{space 1}   -9.89{col 78}{space 3}0.000{col 86}{space 4}-.0914491{col 99}{space 3}-.0608813
{txt}{space 44} {c |}
{space 11}minority#c.ln_ratio_mnmsup_mnmsub {c |}
{space 42}1  {c |}{col 46}{res}{space 2} .0554826{col 58}{space 2} .0206325{col 69}{space 1}    2.69{col 78}{space 3}0.008{col 86}{space 4} .0145533{col 99}{space 3} .0964119
{txt}{space 44} {c |}
{space 32}1.supervisor {c |}{col 46}{res}{space 2} .1412153{col 58}{space 2} .0153985{col 69}{space 1}    9.17{col 78}{space 3}0.000{col 86}{space 4} .1106689{col 99}{space 3} .1717618
{txt}{space 44} {c |}
{space 9}supervisor#c.ln_ratio_mnmsup_mnmsub {c |}
{space 42}1  {c |}{col 46}{res}{space 2} .0411089{col 58}{space 2} .0298773{col 69}{space 1}    1.38{col 78}{space 3}0.172{col 86}{space 4}-.0181596{col 99}{space 3} .1003773
{txt}{space 44} {c |}
{space 25}minority#supervisor {c |}
{space 40}1 1  {c |}{col 46}{res}{space 2} .0124774{col 58}{space 2}  .009036{col 69}{space 1}    1.38{col 78}{space 3}0.170{col 86}{space 4}-.0054477{col 99}{space 3} .0304025
{txt}{space 44} {c |}
minority#supervisor#c.ln_ratio_mnmsup_mnmsub {c |}
{space 40}1 1  {c |}{col 46}{res}{space 2} .0193824{col 58}{space 2} .0187532{col 69}{space 1}    1.03{col 78}{space 3}0.304{col 86}{space 4} -.017819{col 99}{space 3} .0565838
{txt}{space 44} {c |}
{space 19}ln_ratio_min_tot_nmin_tot {c |}{col 46}{res}{space 2} .0580121{col 58}{space 2} .0474395{col 69}{space 1}    1.22{col 78}{space 3}0.224{col 86}{space 4}-.0360952{col 99}{space 3} .1521193
{txt}{space 38}gender {c |}{col 46}{res}{space 2}-.0396323{col 58}{space 2}  .004214{col 69}{space 1}   -9.40{col 78}{space 3}0.000{col 86}{space 4}-.0479918{col 99}{space 3}-.0312729
{txt}{space 28}topoffminority_2 {c |}{col 46}{res}{space 2} .0081539{col 58}{space 2} .0059233{col 69}{space 1}    1.38{col 78}{space 3}0.172{col 86}{space 4}-.0035963{col 99}{space 3} .0199041
{txt}{space 24}lntotworkforce_count {c |}{col 46}{res}{space 2} .0651284{col 58}{space 2} .0420601{col 69}{space 1}    1.55{col 78}{space 3}0.125{col 86}{space 4}-.0183074{col 99}{space 3} .1485642
{txt}{space 16}ln_professionals_total_ratio {c |}{col 46}{res}{space 2} .0213431{col 58}{space 2}  .052063{col 69}{space 1}    0.41{col 78}{space 3}0.683{col 86}{space 4}-.0819359{col 99}{space 3} .1246221
{txt}{space 44} {c |}
{space 36}agencyid {c |}
{space 42}2  {c |}{col 46}{res}{space 2} .2123669{col 58}{space 2} .1293797{col 69}{space 1}    1.64{col 78}{space 3}0.104{col 86}{space 4}-.0442877{col 99}{space 3} .4690216
{txt}{space 42}4  {c |}{col 46}{res}{space 2} .2858402{col 58}{space 2}  .168224{col 69}{space 1}    1.70{col 78}{space 3}0.092{col 86}{space 4} -.047871{col 99}{space 3} .6195514
{txt}{space 42}5  {c |}{col 46}{res}{space 2} .1660697{col 58}{space 2}  .133143{col 69}{space 1}    1.25{col 78}{space 3}0.215{col 86}{space 4}-.0980502{col 99}{space 3} .4301896
{txt}{space 42}7  {c |}{col 46}{res}{space 2} .2409284{col 58}{space 2} .2272558{col 69}{space 1}    1.06{col 78}{space 3}0.292{col 86}{space 4}-.2098859{col 99}{space 3} .6917427
{txt}{space 42}8  {c |}{col 46}{res}{space 2} .2360528{col 58}{space 2} .1698662{col 69}{space 1}    1.39{col 78}{space 3}0.168{col 86}{space 4}-.1009161{col 99}{space 3} .5730218
{txt}{space 42}9  {c |}{col 46}{res}{space 2}-.0569144{col 58}{space 2} .0232868{col 69}{space 1}   -2.44{col 78}{space 3}0.016{col 86}{space 4} -.103109{col 99}{space 3}-.0107197
{txt}{space 41}10  {c |}{col 46}{res}{space 2}  .189918{col 58}{space 2} .2393874{col 69}{space 1}    0.79{col 78}{space 3}0.429{col 86}{space 4}-.2849623{col 99}{space 3} .6647982
{txt}{space 41}11  {c |}{col 46}{res}{space 2} .1639451{col 58}{space 2} .1108444{col 69}{space 1}    1.48{col 78}{space 3}0.142{col 86}{space 4}-.0559404{col 99}{space 3} .3838306
{txt}{space 41}12  {c |}{col 46}{res}{space 2}  .319464{col 58}{space 2} .2211002{col 69}{space 1}    1.44{col 78}{space 3}0.152{col 86}{space 4}-.1191393{col 99}{space 3} .7580673
{txt}{space 41}13  {c |}{col 46}{res}{space 2} .3164288{col 58}{space 2} .1713876{col 69}{space 1}    1.85{col 78}{space 3}0.068{col 86}{space 4} -.023558{col 99}{space 3} .6564156
{txt}{space 41}14  {c |}{col 46}{res}{space 2} .1600185{col 58}{space 2} .1169589{col 69}{space 1}    1.37{col 78}{space 3}0.174{col 86}{space 4}-.0719965{col 99}{space 3} .3920336
{txt}{space 41}15  {c |}{col 46}{res}{space 2} .2296315{col 58}{space 2} .1622739{col 69}{space 1}    1.42{col 78}{space 3}0.160{col 86}{space 4}-.0922763{col 99}{space 3} .5515393
{txt}{space 41}16  {c |}{col 46}{res}{space 2} .1906127{col 58}{space 2} .3022251{col 69}{space 1}    0.63{col 78}{space 3}0.530{col 86}{space 4}-.4089207{col 99}{space 3} .7901461
{txt}{space 41}17  {c |}{col 46}{res}{space 2} .0386935{col 58}{space 2} .1952576{col 69}{space 1}    0.20{col 78}{space 3}0.843{col 86}{space 4}-.3486451{col 99}{space 3}  .426032
{txt}{space 41}18  {c |}{col 46}{res}{space 2} .3105978{col 58}{space 2}  .178066{col 69}{space 1}    1.74{col 78}{space 3}0.084{col 86}{space 4}-.0426373{col 99}{space 3} .6638328
{txt}{space 41}19  {c |}{col 46}{res}{space 2} .1788036{col 58}{space 2} .1209824{col 69}{space 1}    1.48{col 78}{space 3}0.143{col 86}{space 4}-.0611928{col 99}{space 3} .4188001
{txt}{space 41}20  {c |}{col 46}{res}{space 2} .0824296{col 58}{space 2} .1264765{col 69}{space 1}    0.65{col 78}{space 3}0.516{col 86}{space 4}-.1684658{col 99}{space 3}  .333325
{txt}{space 41}21  {c |}{col 46}{res}{space 2}  .211022{col 58}{space 2} .1152491{col 69}{space 1}    1.83{col 78}{space 3}0.070{col 86}{space 4}-.0176012{col 99}{space 3} .4396451
{txt}{space 41}22  {c |}{col 46}{res}{space 2} .1453467{col 58}{space 2}  .182348{col 69}{space 1}    0.80{col 78}{space 3}0.427{col 86}{space 4}-.2163828{col 99}{space 3} .5070762
{txt}{space 41}23  {c |}{col 46}{res}{space 2}-.1107846{col 58}{space 2} .0926107{col 69}{space 1}   -1.20{col 78}{space 3}0.234{col 86}{space 4}-.2944994{col 99}{space 3} .0729302
{txt}{space 41}24  {c |}{col 46}{res}{space 2} .2480507{col 58}{space 2} .1276819{col 69}{space 1}    1.94{col 78}{space 3}0.055{col 86}{space 4}-.0052358{col 99}{space 3} .5013372
{txt}{space 41}25  {c |}{col 46}{res}{space 2} .1845204{col 58}{space 2} .1541165{col 69}{space 1}    1.20{col 78}{space 3}0.234{col 86}{space 4}-.1212053{col 99}{space 3} .4902461
{txt}{space 41}26  {c |}{col 46}{res}{space 2} .0500852{col 58}{space 2} .1356767{col 69}{space 1}    0.37{col 78}{space 3}0.713{col 86}{space 4}-.2190609{col 99}{space 3} .3192312
{txt}{space 41}27  {c |}{col 46}{res}{space 2} .3597196{col 58}{space 2} .2047317{col 69}{space 1}    1.76{col 78}{space 3}0.082{col 86}{space 4} -.046413{col 99}{space 3} .7658521
{txt}{space 41}28  {c |}{col 46}{res}{space 2}-.0537634{col 58}{space 2} .1163263{col 69}{space 1}   -0.46{col 78}{space 3}0.645{col 86}{space 4}-.2845235{col 99}{space 3} .1769966
{txt}{space 41}29  {c |}{col 46}{res}{space 2} .0787326{col 58}{space 2}  .194081{col 69}{space 1}    0.41{col 78}{space 3}0.686{col 86}{space 4}-.3062719{col 99}{space 3} .4637371
{txt}{space 41}30  {c |}{col 46}{res}{space 2} .1570096{col 58}{space 2} .1786529{col 69}{space 1}    0.88{col 78}{space 3}0.382{col 86}{space 4}-.1973897{col 99}{space 3} .5114089
{txt}{space 41}31  {c |}{col 46}{res}{space 2}-.0248792{col 58}{space 2} .1866008{col 69}{space 1}   -0.13{col 78}{space 3}0.894{col 86}{space 4}-.3950449{col 99}{space 3} .3452866
{txt}{space 41}32  {c |}{col 46}{res}{space 2} .2051831{col 58}{space 2} .1490708{col 69}{space 1}    1.38{col 78}{space 3}0.172{col 86}{space 4}-.0905332{col 99}{space 3} .5008994
{txt}{space 41}33  {c |}{col 46}{res}{space 2} .1174318{col 58}{space 2} .0938182{col 69}{space 1}    1.25{col 78}{space 3}0.214{col 86}{space 4}-.0686784{col 99}{space 3} .3035419
{txt}{space 41}34  {c |}{col 46}{res}{space 2}-.1659475{col 58}{space 2} .2395789{col 69}{space 1}   -0.69{col 78}{space 3}0.490{col 86}{space 4}-.6412077{col 99}{space 3} .3093126
{txt}{space 41}35  {c |}{col 46}{res}{space 2} .1426081{col 58}{space 2} .1078042{col 69}{space 1}    1.32{col 78}{space 3}0.189{col 86}{space 4}-.0712465{col 99}{space 3} .3564627
{txt}{space 41}36  {c |}{col 46}{res}{space 2} .2142652{col 58}{space 2} .1421826{col 69}{space 1}    1.51{col 78}{space 3}0.135{col 86}{space 4}-.0677868{col 99}{space 3} .4963173
{txt}{space 41}37  {c |}{col 46}{res}{space 2} .2002018{col 58}{space 2} .1220834{col 69}{space 1}    1.64{col 78}{space 3}0.104{col 86}{space 4}-.0419787{col 99}{space 3} .4423824
{txt}{space 41}38  {c |}{col 46}{res}{space 2} .3095821{col 58}{space 2} .2061082{col 69}{space 1}    1.50{col 78}{space 3}0.136{col 86}{space 4} -.099281{col 99}{space 3} .7184452
{txt}{space 41}39  {c |}{col 46}{res}{space 2} .2211698{col 58}{space 2} .1252143{col 69}{space 1}    1.77{col 78}{space 3}0.080{col 86}{space 4}-.0272216{col 99}{space 3} .4695612
{txt}{space 41}40  {c |}{col 46}{res}{space 2} .0435506{col 58}{space 2} .0809293{col 69}{space 1}    0.54{col 78}{space 3}0.592{col 86}{space 4}-.1169914{col 99}{space 3} .2040926
{txt}{space 41}41  {c |}{col 46}{res}{space 2} .1794469{col 58}{space 2} .1802264{col 69}{space 1}    1.00{col 78}{space 3}0.322{col 86}{space 4}-.1780738{col 99}{space 3} .5369676
{txt}{space 41}42  {c |}{col 46}{res}{space 2} .2848874{col 58}{space 2} .1644532{col 69}{space 1}    1.73{col 78}{space 3}0.086{col 86}{space 4}-.0413436{col 99}{space 3} .6111184
{txt}{space 41}43  {c |}{col 46}{res}{space 2} .0029044{col 58}{space 2} .0777552{col 69}{space 1}    0.04{col 78}{space 3}0.970{col 86}{space 4} -.151341{col 99}{space 3} .1571498
{txt}{space 41}44  {c |}{col 46}{res}{space 2} .2561736{col 58}{space 2} .1419859{col 69}{space 1}    1.80{col 78}{space 3}0.074{col 86}{space 4}-.0254882{col 99}{space 3} .5378353
{txt}{space 41}45  {c |}{col 46}{res}{space 2} .2208379{col 58}{space 2} .1273808{col 69}{space 1}    1.73{col 78}{space 3}0.086{col 86}{space 4}-.0318514{col 99}{space 3} .4735272
{txt}{space 41}47  {c |}{col 46}{res}{space 2} .1528312{col 58}{space 2} .0948681{col 69}{space 1}    1.61{col 78}{space 3}0.110{col 86}{space 4}-.0353616{col 99}{space 3} .3410239
{txt}{space 41}48  {c |}{col 46}{res}{space 2} .2363531{col 58}{space 2} .1942497{col 69}{space 1}    1.22{col 78}{space 3}0.227{col 86}{space 4} -.148986{col 99}{space 3} .6216922
{txt}{space 41}49  {c |}{col 46}{res}{space 2} .3357684{col 58}{space 2} .2138252{col 69}{space 1}    1.57{col 78}{space 3}0.119{col 86}{space 4}-.0884033{col 99}{space 3}   .75994
{txt}{space 41}50  {c |}{col 46}{res}{space 2} .3457895{col 58}{space 2} .1943218{col 69}{space 1}    1.78{col 78}{space 3}0.078{col 86}{space 4}-.0396927{col 99}{space 3} .7312717
{txt}{space 41}51  {c |}{col 46}{res}{space 2} .3372898{col 58}{space 2} .2403624{col 69}{space 1}    1.40{col 78}{space 3}0.164{col 86}{space 4}-.1395245{col 99}{space 3} .8141041
{txt}{space 41}52  {c |}{col 46}{res}{space 2} .2330343{col 58}{space 2} .2414525{col 69}{space 1}    0.97{col 78}{space 3}0.337{col 86}{space 4}-.2459424{col 99}{space 3} .7120111
{txt}{space 41}53  {c |}{col 46}{res}{space 2}  .282142{col 58}{space 2} .1720085{col 69}{space 1}    1.64{col 78}{space 3}0.104{col 86}{space 4}-.0590767{col 99}{space 3} .6233606
{txt}{space 41}54  {c |}{col 46}{res}{space 2} .2926327{col 58}{space 2} .1929459{col 69}{space 1}    1.52{col 78}{space 3}0.132{col 86}{space 4}-.0901201{col 99}{space 3} .6753855
{txt}{space 41}55  {c |}{col 46}{res}{space 2} .4201199{col 58}{space 2} .2472716{col 69}{space 1}    1.70{col 78}{space 3}0.092{col 86}{space 4}-.0704004{col 99}{space 3} .9106402
{txt}{space 41}56  {c |}{col 46}{res}{space 2} .2443525{col 58}{space 2} .2493085{col 69}{space 1}    0.98{col 78}{space 3}0.329{col 86}{space 4}-.2502085{col 99}{space 3} .7389134
{txt}{space 41}57  {c |}{col 46}{res}{space 2} .3094207{col 58}{space 2} .3142936{col 69}{space 1}    0.98{col 78}{space 3}0.327{col 86}{space 4}-.3140533{col 99}{space 3} .9328948
{txt}{space 41}58  {c |}{col 46}{res}{space 2} .1962065{col 58}{space 2} .1826552{col 69}{space 1}    1.07{col 78}{space 3}0.285{col 86}{space 4}-.1661321{col 99}{space 3} .5585452
{txt}{space 41}59  {c |}{col 46}{res}{space 2} .1880874{col 58}{space 2} .2201931{col 69}{space 1}    0.85{col 78}{space 3}0.395{col 86}{space 4}-.2487164{col 99}{space 3} .6248912
{txt}{space 41}60  {c |}{col 46}{res}{space 2} .1459325{col 58}{space 2}  .109414{col 69}{space 1}    1.33{col 78}{space 3}0.185{col 86}{space 4}-.0711154{col 99}{space 3} .3629804
{txt}{space 41}61  {c |}{col 46}{res}{space 2} .1361642{col 58}{space 2} .1166897{col 69}{space 1}    1.17{col 78}{space 3}0.246{col 86}{space 4}-.0953167{col 99}{space 3} .3676452
{txt}{space 41}62  {c |}{col 46}{res}{space 2} .3214992{col 58}{space 2} .2167329{col 69}{space 1}    1.48{col 78}{space 3}0.141{col 86}{space 4}-.1084406{col 99}{space 3}  .751439
{txt}{space 41}63  {c |}{col 46}{res}{space 2} .3894671{col 58}{space 2} .2118841{col 69}{space 1}    1.84{col 78}{space 3}0.069{col 86}{space 4}-.0308539{col 99}{space 3} .8097881
{txt}{space 41}64  {c |}{col 46}{res}{space 2} .4023302{col 58}{space 2} .2222352{col 69}{space 1}    1.81{col 78}{space 3}0.073{col 86}{space 4}-.0385246{col 99}{space 3}  .843185
{txt}{space 41}65  {c |}{col 46}{res}{space 2} .2107726{col 58}{space 2} .1272017{col 69}{space 1}    1.66{col 78}{space 3}0.101{col 86}{space 4}-.0415614{col 99}{space 3} .4631066
{txt}{space 41}66  {c |}{col 46}{res}{space 2}  .187959{col 58}{space 2} .2353464{col 69}{space 1}    0.80{col 78}{space 3}0.426{col 86}{space 4} -.278905{col 99}{space 3}  .654823
{txt}{space 41}67  {c |}{col 46}{res}{space 2} .2247024{col 58}{space 2}  .143186{col 69}{space 1}    1.57{col 78}{space 3}0.120{col 86}{space 4}-.0593401{col 99}{space 3}  .508745
{txt}{space 41}68  {c |}{col 46}{res}{space 2} .2793696{col 58}{space 2} .1591775{col 69}{space 1}    1.76{col 78}{space 3}0.082{col 86}{space 4}-.0363957{col 99}{space 3} .5951349
{txt}{space 41}69  {c |}{col 46}{res}{space 2} .1397347{col 58}{space 2} .1293083{col 69}{space 1}    1.08{col 78}{space 3}0.282{col 86}{space 4}-.1167781{col 99}{space 3} .3962475
{txt}{space 41}70  {c |}{col 46}{res}{space 2} .3463405{col 58}{space 2} .2195663{col 69}{space 1}    1.58{col 78}{space 3}0.118{col 86}{space 4}-.0892199{col 99}{space 3} .7819009
{txt}{space 41}71  {c |}{col 46}{res}{space 2}-.1212219{col 58}{space 2} .1916835{col 69}{space 1}   -0.63{col 78}{space 3}0.529{col 86}{space 4}-.5014704{col 99}{space 3} .2590266
{txt}{space 41}72  {c |}{col 46}{res}{space 2} .2019495{col 58}{space 2} .1241735{col 69}{space 1}    1.63{col 78}{space 3}0.107{col 86}{space 4}-.0443773{col 99}{space 3} .4482763
{txt}{space 41}73  {c |}{col 46}{res}{space 2} .4261758{col 58}{space 2} .2020437{col 69}{space 1}    2.11{col 78}{space 3}0.037{col 86}{space 4} .0253756{col 99}{space 3} .8269761
{txt}{space 41}74  {c |}{col 46}{res}{space 2} .1563641{col 58}{space 2} .1068776{col 69}{space 1}    1.46{col 78}{space 3}0.147{col 86}{space 4}-.0556523{col 99}{space 3} .3683806
{txt}{space 41}75  {c |}{col 46}{res}{space 2} .0634305{col 58}{space 2} .1577852{col 69}{space 1}    0.40{col 78}{space 3}0.689{col 86}{space 4}-.2495729{col 99}{space 3} .3764339
{txt}{space 41}76  {c |}{col 46}{res}{space 2} .2972743{col 58}{space 2} .1822364{col 69}{space 1}    1.63{col 78}{space 3}0.106{col 86}{space 4}-.0642336{col 99}{space 3} .6587822
{txt}{space 41}77  {c |}{col 46}{res}{space 2} .2216786{col 58}{space 2} .1784935{col 69}{space 1}    1.24{col 78}{space 3}0.217{col 86}{space 4}-.1324045{col 99}{space 3} .5757617
{txt}{space 41}78  {c |}{col 46}{res}{space 2} .2694444{col 58}{space 2} .1160535{col 69}{space 1}    2.32{col 78}{space 3}0.022{col 86}{space 4} .0392254{col 99}{space 3} .4996633
{txt}{space 41}79  {c |}{col 46}{res}{space 2}-.0178616{col 58}{space 2} .0257745{col 69}{space 1}   -0.69{col 78}{space 3}0.490{col 86}{space 4}-.0689912{col 99}{space 3} .0332681
{txt}{space 41}80  {c |}{col 46}{res}{space 2}   .43477{col 58}{space 2} .2251266{col 69}{space 1}    1.93{col 78}{space 3}0.056{col 86}{space 4}-.0118206{col 99}{space 3} .8813605
{txt}{space 41}81  {c |}{col 46}{res}{space 2} .1612572{col 58}{space 2}  .248669{col 69}{space 1}    0.65{col 78}{space 3}0.518{col 86}{space 4}-.3320351{col 99}{space 3} .6545496
{txt}{space 41}82  {c |}{col 46}{res}{space 2} .2355118{col 58}{space 2}  .214455{col 69}{space 1}    1.10{col 78}{space 3}0.275{col 86}{space 4}-.1899093{col 99}{space 3} .6609328
{txt}{space 41}83  {c |}{col 46}{res}{space 2} .3454136{col 58}{space 2} .1781048{col 69}{space 1}    1.94{col 78}{space 3}0.055{col 86}{space 4}-.0078985{col 99}{space 3} .6987257
{txt}{space 41}84  {c |}{col 46}{res}{space 2} .2842827{col 58}{space 2} .2179948{col 69}{space 1}    1.30{col 78}{space 3}0.195{col 86}{space 4}-.1481603{col 99}{space 3} .7167257
{txt}{space 41}85  {c |}{col 46}{res}{space 2} .3031855{col 58}{space 2} .1672929{col 69}{space 1}    1.81{col 78}{space 3}0.073{col 86}{space 4}-.0286786{col 99}{space 3} .6350496
{txt}{space 41}86  {c |}{col 46}{res}{space 2} .3229801{col 58}{space 2} .2522174{col 69}{space 1}    1.28{col 78}{space 3}0.203{col 86}{space 4}-.1773514{col 99}{space 3} .8233115
{txt}{space 41}87  {c |}{col 46}{res}{space 2} .3804539{col 58}{space 2} .2494973{col 69}{space 1}    1.52{col 78}{space 3}0.130{col 86}{space 4}-.1144817{col 99}{space 3} .8753895
{txt}{space 41}88  {c |}{col 46}{res}{space 2}  .166961{col 58}{space 2} .1761231{col 69}{space 1}    0.95{col 78}{space 3}0.345{col 86}{space 4}-.1824198{col 99}{space 3} .5163419
{txt}{space 41}89  {c |}{col 46}{res}{space 2} .2308032{col 58}{space 2} .1730515{col 69}{space 1}    1.33{col 78}{space 3}0.185{col 86}{space 4}-.1124845{col 99}{space 3} .5740909
{txt}{space 41}90  {c |}{col 46}{res}{space 2} .0145656{col 58}{space 2} .0889043{col 69}{space 1}    0.16{col 78}{space 3}0.870{col 86}{space 4}-.1617966{col 99}{space 3} .1909279
{txt}{space 41}91  {c |}{col 46}{res}{space 2} .1415756{col 58}{space 2} .1240005{col 69}{space 1}    1.14{col 78}{space 3}0.256{col 86}{space 4}-.1044082{col 99}{space 3} .3875593
{txt}{space 41}92  {c |}{col 46}{res}{space 2} .0793004{col 58}{space 2} .0620387{col 69}{space 1}    1.28{col 78}{space 3}0.204{col 86}{space 4}-.0437677{col 99}{space 3} .2023685
{txt}{space 41}93  {c |}{col 46}{res}{space 2} .3544754{col 58}{space 2} .1822091{col 69}{space 1}    1.95{col 78}{space 3}0.055{col 86}{space 4}-.0069785{col 99}{space 3} .7159293
{txt}{space 41}94  {c |}{col 46}{res}{space 2} .4507732{col 58}{space 2} .2210154{col 69}{space 1}    2.04{col 78}{space 3}0.044{col 86}{space 4} .0123382{col 99}{space 3} .8892083
{txt}{space 41}95  {c |}{col 46}{res}{space 2} .1638838{col 58}{space 2} .2179463{col 69}{space 1}    0.75{col 78}{space 3}0.454{col 86}{space 4}-.2684631{col 99}{space 3} .5962306
{txt}{space 41}96  {c |}{col 46}{res}{space 2} .2980975{col 58}{space 2}  .196262{col 69}{space 1}    1.52{col 78}{space 3}0.132{col 86}{space 4}-.0912335{col 99}{space 3} .6874285
{txt}{space 41}97  {c |}{col 46}{res}{space 2} .3335456{col 58}{space 2} .1610665{col 69}{space 1}    2.07{col 78}{space 3}0.041{col 86}{space 4}  .014033{col 99}{space 3} .6530582
{txt}{space 41}98  {c |}{col 46}{res}{space 2} .4421488{col 58}{space 2} .2325827{col 69}{space 1}    1.90{col 78}{space 3}0.060{col 86}{space 4}-.0192328{col 99}{space 3} .9035304
{txt}{space 41}99  {c |}{col 46}{res}{space 2} .0407382{col 58}{space 2} .0592117{col 69}{space 1}    0.69{col 78}{space 3}0.493{col 86}{space 4} -.076722{col 99}{space 3} .1581984
{txt}{space 40}100  {c |}{col 46}{res}{space 2} .2064064{col 58}{space 2}  .219113{col 69}{space 1}    0.94{col 78}{space 3}0.348{col 86}{space 4}-.2282548{col 99}{space 3} .6410676
{txt}{space 40}101  {c |}{col 46}{res}{space 2} .3674497{col 58}{space 2} .1730253{col 69}{space 1}    2.12{col 78}{space 3}0.036{col 86}{space 4} .0242141{col 99}{space 3} .7106854
{txt}{space 40}102  {c |}{col 46}{res}{space 2} .2745215{col 58}{space 2} .2215092{col 69}{space 1}    1.24{col 78}{space 3}0.218{col 86}{space 4}-.1648933{col 99}{space 3} .7139362
{txt}{space 40}103  {c |}{col 46}{res}{space 2} .0775701{col 58}{space 2} .1218596{col 69}{space 1}    0.64{col 78}{space 3}0.526{col 86}{space 4}-.1641665{col 99}{space 3} .3193067
{txt}{space 40}104  {c |}{col 46}{res}{space 2}-.1609526{col 58}{space 2} .0610625{col 69}{space 1}   -2.64{col 78}{space 3}0.010{col 86}{space 4}-.2820841{col 99}{space 3}-.0398211
{txt}{space 40}105  {c |}{col 46}{res}{space 2} .2252309{col 58}{space 2} .2082945{col 69}{space 1}    1.08{col 78}{space 3}0.282{col 86}{space 4}-.1879693{col 99}{space 3} .6384311
{txt}{space 44} {c |}
{space 40}year {c |}
{space 39}2011  {c |}{col 46}{res}{space 2}-.0018736{col 58}{space 2} .0038763{col 69}{space 1}   -0.48{col 78}{space 3}0.630{col 86}{space 4}-.0095632{col 99}{space 3} .0058159
{txt}{space 39}2012  {c |}{col 46}{res}{space 2} .0053776{col 58}{space 2} .0047043{col 69}{space 1}    1.14{col 78}{space 3}0.256{col 86}{space 4}-.0039545{col 99}{space 3} .0147098
{txt}{space 39}2013  {c |}{col 46}{res}{space 2}  .002979{col 58}{space 2}   .00585{col 69}{space 1}    0.51{col 78}{space 3}0.612{col 86}{space 4}-.0086258{col 99}{space 3} .0145838
{txt}{space 39}2014  {c |}{col 46}{res}{space 2} -.002258{col 58}{space 2} .0069259{col 69}{space 1}   -0.33{col 78}{space 3}0.745{col 86}{space 4} -.015997{col 99}{space 3} .0114811
{txt}{space 39}2015  {c |}{col 46}{res}{space 2}-.0104111{col 58}{space 2} .0083547{col 69}{space 1}   -1.25{col 78}{space 3}0.216{col 86}{space 4}-.0269846{col 99}{space 3} .0061623
{txt}{space 39}2016  {c |}{col 46}{res}{space 2}-.0132193{col 58}{space 2} .0082945{col 69}{space 1}   -1.59{col 78}{space 3}0.114{col 86}{space 4}-.0296733{col 99}{space 3} .0032346
{txt}{space 39}2017  {c |}{col 46}{res}{space 2}-.0031059{col 58}{space 2} .0121087{col 69}{space 1}   -0.26{col 78}{space 3}0.798{col 86}{space 4}-.0271263{col 99}{space 3} .0209145
{txt}{space 39}2018  {c |}{col 46}{res}{space 2} -.029322{col 58}{space 2} .0111578{col 69}{space 1}   -2.63{col 78}{space 3}0.010{col 86}{space 4} -.051456{col 99}{space 3}-.0071879
{txt}{space 39}2019  {c |}{col 46}{res}{space 2}-.0686446{col 58}{space 2} .0125695{col 69}{space 1}   -5.46{col 78}{space 3}0.000{col 86}{space 4}-.0935791{col 99}{space 3}-.0437102
{txt}{space 44} {c |}
{space 39}_cons {c |}{col 46}{res}{space 2} .0429991{col 58}{space 2} .5324338{col 69}{space 1}    0.08{col 78}{space 3}0.936{col 86}{space 4}-1.013206{col 99}{space 3} 1.099205
{txt}{hline 45}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16} 2,454,819{col 28} -1910867{col 39} -1860154{col 50}    21{col 58}  3720351{col 69}  3720618
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] IC note}}.{p_end}

{com}. *
. 
. ** BY NON-SUPERVISORS RESPONDENT: WITHIN-IDENTITY "OUT" GROUP STATUS DIFFERENTIAL BETWEEN MINORITY/NON-MINORITY RESPONDENTS **
. 
. lincom c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0445514{col 26}{space 2} .0365177{col 37}{space 1}    1.22{col 46}{space 3}0.225{col 54}{space 4}-.0278899{col 67}{space 3} .1169926
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  1.minority#c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.minority#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0554826{col 26}{space 2} .0206325{col 37}{space 1}    2.69{col 46}{space 3}0.008{col 54}{space 4} .0145533{col 67}{space 3} .0964119
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *
. *
. *
. *
. 
. ** BY SUPERVISOR RESPONDENT: WITHIN-IDENTITY "OUT" GROUP STATUS DIFFERENTIAL BETWEEN MINORITY/NON-MINORITY RESPONDENTS **
. 
. lincom c.ln_ratio_mnmsup_mnmsub + 1.supervisor#c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}ln_ratio_mnmsup_mnmsub + 1.supervisor#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0856602{col 26}{space 2} .0443644{col 37}{space 1}    1.93{col 46}{space 3}0.056{col 54}{space 4}-.0023468{col 67}{space 3} .1736672
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  1.minority#c.ln_ratio_mnmsup_mnmsub +  1.minority#1.supervisor#c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.minority#c.ln_ratio_mnmsup_mnmsub + 1.minority#1.supervisor#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}  .074865{col 26}{space 2} .0193376{col 37}{space 1}    3.87{col 46}{space 3}0.000{col 54}{space 4} .0365044{col 67}{space 3} .1132255
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. * SUPERVISOR - NON-SUPERVISORY DIFFERENCE AMONG MINORITY RESPONDENT DIFFERENCES 
.  
. lincom  1.minority#c.ln_ratio_mnmsup_mnmsub +  1.minority#1.supervisor#c.ln_ratio_mnmsup_mnmsub - (1.minority#c.ln_ratio_mnmsup_mnmsub)

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.minority#1.supervisor#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0193824{col 26}{space 2} .0187532{col 37}{space 1}    1.03{col 46}{space 3}0.304{col 54}{space 4} -.017819{col 67}{space 3} .0565838
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. *********************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
.    
. *** MODEL G7.2: CONDITIONAL RESPONSES BY GENDER & POSITION -- GENDER BETWEEN-IDENTITY GROUP STATUS DIFFERENTIAL MODEL: [WOMEN SUPERVISORS WITHIN AGENCY j IN YEAR t / MEN SUPERVISORS WITHIN AGENCY j IN YEAR t] / [WOMEN NON-SUPERVISORS WITHIN AGENCY j IN YEAR t / MEN NON-SUPERVISORS WITHIN AGENCY j IN YEAR t] -- CONTROLLING FOR GENDER SUPERVISORY EMPLOYEE IDENTITY GROUP DIFFERENTIAL ***
. 
. regress lndiversity2zeroadj  c.ln_ratio_fmsup_fmsub##i.women_het##i.supervisor   ln_ratio_fem_tot_men_tot   minority   topoffgender_2 lntotworkforce_count  ln_professionals_total_ratio   i.agencyid i.year if ln_ratio_fmsup_fmsub <= 0, vce(cluster agencyid)

{txt}Linear regression                               Number of obs     = {res} 2,463,525
                                                {txt}{help j_robustsingular:F(24, 102) }       =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0404
                                                {txt}Root MSE          =    {res} .51451

{txt}{ralign 109:(Std. err. adjusted for {res:103} clusters in {res:agencyid})}
{hline 44}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 45}{c |}{col 57}    Robust
{col 1}                        lndiversity2zeroadj{col 45}{c |} Coefficient{col 57}  std. err.{col 69}      t{col 77}   P>|t|{col 85}     [95% con{col 98}f. interval]
{hline 44}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}ln_ratio_fmsup_fmsub {c |}{col 45}{res}{space 2} .1317732{col 57}{space 2} .0467182{col 68}{space 1}    2.82{col 77}{space 3}0.006{col 85}{space 4} .0391079{col 98}{space 3} .2244385
{txt}{space 43} {c |}
{space 34}women_het {c |}
{space 41}1  {c |}{col 45}{res}{space 2}-.0246952{col 57}{space 2} .0122988{col 68}{space 1}   -2.01{col 77}{space 3}0.047{col 85}{space 4}-.0490898{col 98}{space 3}-.0003006
{txt}{space 41}2  {c |}{col 45}{res}{space 2} -.053439{col 57}{space 2}  .011291{col 68}{space 1}   -4.73{col 77}{space 3}0.000{col 85}{space 4}-.0758345{col 98}{space 3}-.0310434
{txt}{space 43} {c |}
{space 11}women_het#c.ln_ratio_fmsup_fmsub {c |}
{space 41}1  {c |}{col 45}{res}{space 2} .0066398{col 57}{space 2} .0304231{col 68}{space 1}    0.22{col 77}{space 3}0.828{col 85}{space 4}-.0537043{col 98}{space 3} .0669839
{txt}{space 41}2  {c |}{col 45}{res}{space 2} .0340096{col 57}{space 2} .0300358{col 68}{space 1}    1.13{col 77}{space 3}0.260{col 85}{space 4}-.0255662{col 98}{space 3} .0935854
{txt}{space 43} {c |}
{space 31}1.supervisor {c |}{col 45}{res}{space 2} .1479553{col 57}{space 2} .0227556{col 68}{space 1}    6.50{col 77}{space 3}0.000{col 85}{space 4} .1028198{col 98}{space 3} .1930909
{txt}{space 43} {c |}
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{txt}{space 40}92  {c |}{col 45}{res}{space 2} .0877977{col 57}{space 2} .0458007{col 68}{space 1}    1.92{col 77}{space 3}0.058{col 85}{space 4}-.0030478{col 98}{space 3} .1786433
{txt}{space 40}93  {c |}{col 45}{res}{space 2} .4606462{col 57}{space 2} .1541245{col 68}{space 1}    2.99{col 77}{space 3}0.004{col 85}{space 4}  .154941{col 98}{space 3} .7663514
{txt}{space 40}94  {c |}{col 45}{res}{space 2} .5234248{col 57}{space 2} .1742352{col 68}{space 1}    3.00{col 77}{space 3}0.003{col 85}{space 4} .1778302{col 98}{space 3} .8690195
{txt}{space 40}95  {c |}{col 45}{res}{space 2} .2439711{col 57}{space 2} .1508678{col 68}{space 1}    1.62{col 77}{space 3}0.109{col 85}{space 4}-.0552744{col 98}{space 3} .5432166
{txt}{space 40}96  {c |}{col 45}{res}{space 2} .3749603{col 57}{space 2} .1618167{col 68}{space 1}    2.32{col 77}{space 3}0.022{col 85}{space 4} .0539977{col 98}{space 3} .6959229
{txt}{space 40}97  {c |}{col 45}{res}{space 2} .4271691{col 57}{space 2} .1548575{col 68}{space 1}    2.76{col 77}{space 3}0.007{col 85}{space 4} .1200099{col 98}{space 3} .7343283
{txt}{space 40}98  {c |}{col 45}{res}{space 2} .5997694{col 57}{space 2} .1935585{col 68}{space 1}    3.10{col 77}{space 3}0.003{col 85}{space 4}  .215847{col 98}{space 3} .9836919
{txt}{space 40}99  {c |}{col 45}{res}{space 2} .0990721{col 57}{space 2}  .092787{col 68}{space 1}    1.07{col 77}{space 3}0.288{col 85}{space 4}-.0849704{col 98}{space 3} .2831146
{txt}{space 39}100  {c |}{col 45}{res}{space 2} .2837266{col 57}{space 2} .1538481{col 68}{space 1}    1.84{col 77}{space 3}0.068{col 85}{space 4}-.0214304{col 98}{space 3} .5888836
{txt}{space 39}101  {c |}{col 45}{res}{space 2} .4328142{col 57}{space 2} .1387665{col 68}{space 1}    3.12{col 77}{space 3}0.002{col 85}{space 4} .1575716{col 98}{space 3} .7080568
{txt}{space 39}103  {c |}{col 45}{res}{space 2} .1099664{col 57}{space 2} .1085897{col 68}{space 1}    1.01{col 77}{space 3}0.314{col 85}{space 4}-.1054208{col 98}{space 3} .3253536
{txt}{space 39}104  {c |}{col 45}{res}{space 2}-.1011053{col 57}{space 2} .0767048{col 68}{space 1}   -1.32{col 77}{space 3}0.190{col 85}{space 4}-.2532489{col 98}{space 3} .0510383
{txt}{space 39}105  {c |}{col 45}{res}{space 2} .3689162{col 57}{space 2} .1593875{col 68}{space 1}    2.31{col 77}{space 3}0.023{col 85}{space 4}  .052772{col 98}{space 3} .6850605
{txt}{space 43} {c |}
{space 39}year {c |}
{space 38}2011  {c |}{col 45}{res}{space 2}-.0043158{col 57}{space 2} .0032558{col 68}{space 1}   -1.33{col 77}{space 3}0.188{col 85}{space 4}-.0107736{col 98}{space 3}  .002142
{txt}{space 38}2012  {c |}{col 45}{res}{space 2} -.000687{col 57}{space 2} .0045866{col 68}{space 1}   -0.15{col 77}{space 3}0.881{col 85}{space 4}-.0097845{col 98}{space 3} .0084105
{txt}{space 38}2013  {c |}{col 45}{res}{space 2}-.0018348{col 57}{space 2}  .005081{col 68}{space 1}   -0.36{col 77}{space 3}0.719{col 85}{space 4} -.011913{col 98}{space 3} .0082433
{txt}{space 38}2014  {c |}{col 45}{res}{space 2}-.0075671{col 57}{space 2} .0072043{col 68}{space 1}   -1.05{col 77}{space 3}0.296{col 85}{space 4}-.0218568{col 98}{space 3} .0067225
{txt}{space 38}2015  {c |}{col 45}{res}{space 2}-.0145837{col 57}{space 2} .0088007{col 68}{space 1}   -1.66{col 77}{space 3}0.101{col 85}{space 4}-.0320399{col 98}{space 3} .0028725
{txt}{space 38}2016  {c |}{col 45}{res}{space 2}-.0184472{col 57}{space 2} .0076611{col 68}{space 1}   -2.41{col 77}{space 3}0.018{col 85}{space 4} -.033643{col 98}{space 3}-.0032514
{txt}{space 38}2017  {c |}{col 45}{res}{space 2}-.0037268{col 57}{space 2} .0099051{col 68}{space 1}   -0.38{col 77}{space 3}0.708{col 85}{space 4}-.0233734{col 98}{space 3} .0159199
{txt}{space 38}2018  {c |}{col 45}{res}{space 2}-.0332531{col 57}{space 2} .0074914{col 68}{space 1}   -4.44{col 77}{space 3}0.000{col 85}{space 4}-.0481123{col 98}{space 3}-.0183938
{txt}{space 38}2019  {c |}{col 45}{res}{space 2}-.0726422{col 57}{space 2} .0085703{col 68}{space 1}   -8.48{col 77}{space 3}0.000{col 85}{space 4}-.0896413{col 98}{space 3} -.055643
{txt}{space 43} {c |}
{space 38}_cons {c |}{col 45}{res}{space 2}-.2271886{col 57}{space 2} .4358093{col 68}{space 1}   -0.52{col 77}{space 3}0.603{col 85}{space 4}-1.091614{col 98}{space 3} .6372371
{txt}{hline 44}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16} 2,463,525{col 28} -1909279{col 39} -1858424{col 50}    25{col 58}  3716898{col 69}  3717216
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] IC note}}.{p_end}

{com}. *
. ** BY NON-SUPERVISORS RESPONDENT: BETWEEN-IDENTITY "IN" GROUP VERSUS "OUT" GROUP STATUS DIFFERENTIAL BETWEEN GENDERED RESPONDENTS **
. 
. lincom c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .1317732{col 26}{space 2} .0467182{col 37}{space 1}    2.82{col 46}{space 3}0.006{col 54}{space 4} .0391079{col 67}{space 3} .2244385
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  1.women_het#c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.women_het#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0066398{col 26}{space 2} .0304231{col 37}{space 1}    0.22{col 46}{space 3}0.828{col 54}{space 4}-.0537043{col 67}{space 3} .0669839
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  2.women_het#c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}2.women_het#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0340096{col 26}{space 2} .0300358{col 37}{space 1}    1.13{col 46}{space 3}0.260{col 54}{space 4}-.0255662{col 67}{space 3} .0935854
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. lincom 2.women_het#c.ln_ratio_fmsup_fmsub -  1.women_het#c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1}{space 1}{res}- 1.women_het#c.ln_ratio_fmsup_fmsub + 2.women_het#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0273698{col 26}{space 2} .0210608{col 37}{space 1}    1.30{col 46}{space 3}0.197{col 54}{space 4}-.0144043{col 67}{space 3} .0691439
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. *
. 
. 
. ** BY SUPERVISOR RESPONDENT:BETWEEN-IDENTITY "IN" GROUP VERSUS "OUT" GROUP STATUS DIFFERENTIAL BETWEENGENDERED RESPONDENTS **
. 
. lincom c.ln_ratio_fmsup_fmsub + 1.supervisor#c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}ln_ratio_fmsup_fmsub + 1.supervisor#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .1945257{col 26}{space 2} .0506854{col 37}{space 1}    3.84{col 46}{space 3}0.000{col 54}{space 4} .0939914{col 67}{space 3}   .29506
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  1.women_het#c.ln_ratio_fmsup_fmsub +  1.women_het#1.supervisor#c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.women_het#c.ln_ratio_fmsup_fmsub + 1.women_het#1.supervisor#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0085904{col 26}{space 2}  .016263{col 37}{space 1}    0.53{col 46}{space 3}0.598{col 54}{space 4}-.0236671{col 67}{space 3} .0408479
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  2.women_het#c.ln_ratio_fmsup_fmsub +  2.women_het#1.supervisor#c.ln_ratio_fmsup_fmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}2.women_het#c.ln_ratio_fmsup_fmsub + 2.women_het#1.supervisor#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.0013054{col 26}{space 2} .0370834{col 37}{space 1}   -0.04{col 46}{space 3}0.972{col 54}{space 4}-.0748602{col 67}{space 3} .0722495
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. lincom  2.women_het#c.ln_ratio_fmsup_fmsub +  2.women_het#1.supervisor#c.ln_ratio_fmsup_fmsub - (1.women_het#c.ln_ratio_fmsup_fmsub +  1.women_het#1.supervisor#c.ln_ratio_fmsup_fmsub)

{p 0 7}{space 1}{text:( 1)}{space 1}{space 1}{res}- 1.women_het#c.ln_ratio_fmsup_fmsub + 2.women_het#c.ln_ratio_fmsup_fmsub - 1.women_het#1.supervisor#c.ln_ratio_fmsup_fmsub + 2.women_het#1.supervisor#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.0098958{col 26}{space 2} .0328358{col 37}{space 1}   -0.30{col 46}{space 3}0.764{col 54}{space 4}-.0750254{col 67}{space 3} .0552338
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. * SUPERVISOR - NON-SUPERVISORY DIFFERENCE AMONG WOMEN RESPONDENT DIFFERENCES [NON-MINORITY WOMEN RESPONDENTS FOLLOWED BY MINORITY WOMEN RESPONDENTS] -- DO NOT PLOT IN GRAPHS [ONLY FOR TEXT]!
. 
. lincom  1.women_het#c.ln_ratio_fmsup_fmsub +  1.women_het#1.supervisor#c.ln_ratio_fmsup_fmsub  - (1.women_het#c.ln_ratio_fmsup_fmsub)

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.women_het#1.supervisor#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0019506{col 26}{space 2} .0314003{col 37}{space 1}    0.06{col 46}{space 3}0.951{col 54}{space 4}-.0603316{col 67}{space 3} .0642329
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. lincom  2.women_het#c.ln_ratio_fmsup_fmsub +  2.women_het#1.supervisor#c.ln_ratio_fmsup_fmsub  - (2.women_het#c.ln_ratio_fmsup_fmsub)

{p 0 7}{space 1}{text:( 1)}{space 1} {res}2.women_het#1.supervisor#c.ln_ratio_fmsup_fmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} -.035315{col 26}{space 2} .0441893{col 37}{space 1}   -0.80{col 46}{space 3}0.426{col 54}{space 4}-.1229642{col 67}{space 3} .0523342
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. 
. 
. 
.    
. *** MODEL G8.2: CONDITIONAL RESPONSES BY RACE/ETHNICITY & POSITION -- RACIAL/ETHNIC BETWEEN-IDENTITY GROUP STATUS DIFFERENTIAL MODEL: [MINORITY SUPERVISORS WITHIN AGENCY j IN YEAR t / NON-MINORITY SUPERVISORS WITHIN AGENCY j IN YEAR t] / [MINORITY NON-SUPERVISORS WITHIN AGENCY j IN YEAR t / NON-MINORITY NON-SUPERVISORS WITHIN AGENCY j IN YEAR t] -- CONTROLLING FOR RACIAL/ETHNIC SUPERVISORY EMPLOYEE IDENTITY GROUP DIFFERENTIAL  ***
. 
. regress  lndiversity2zeroadj  c.ln_ratio_mnmsup_mnmsub##i.minority_het##i.supervisor  ln_ratio_min_tot_nmin_tot   gender  topoffminority_2 lntotworkforce_count  ln_professionals_total_ratio   i.agencyid i.year if e(sample) &  ln_ratio_mnmsup_mnmsub<=0, vce(cluster agencyid)

{txt}Linear regression                               Number of obs     = {res} 2,412,654
                                                {txt}{help j_robustsingular:F(24, 99) }        =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0404
                                                {txt}Root MSE          =    {res} .51658

{txt}{ralign 114:(Std. err. adjusted for {res:100} clusters in {res:agencyid})}
{hline 49}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 50}{c |}{col 62}    Robust
{col 1}                             lndiversity2zeroadj{col 50}{c |} Coefficient{col 62}  std. err.{col 74}      t{col 82}   P>|t|{col 90}     [95% con{col 103}f. interval]
{hline 49}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}ln_ratio_mnmsup_mnmsub {c |}{col 50}{res}{space 2} .0375523{col 62}{space 2} .0361836{col 73}{space 1}    1.04{col 82}{space 3}0.302{col 90}{space 4}-.0342438{col 103}{space 3} .1093485
{txt}{space 48} {c |}
{space 36}minority_het {c |}
{space 46}1  {c |}{col 50}{res}{space 2}-.0674208{col 62}{space 2} .0066864{col 73}{space 1}  -10.08{col 82}{space 3}0.000{col 90}{space 4} -.080688{col 103}{space 3}-.0541536
{txt}{space 46}2  {c |}{col 50}{res}{space 2}-.0911353{col 62}{space 2} .0095824{col 73}{space 1}   -9.51{col 82}{space 3}0.000{col 90}{space 4}-.1101488{col 103}{space 3}-.0721218
{txt}{space 48} {c |}
{space 11}minority_het#c.ln_ratio_mnmsup_mnmsub {c |}
{space 46}1  {c |}{col 50}{res}{space 2}  .024794{col 62}{space 2}   .01839{col 73}{space 1}    1.35{col 82}{space 3}0.181{col 90}{space 4}-.0116958{col 103}{space 3} .0612837
{txt}{space 46}2  {c |}{col 50}{res}{space 2} .0664044{col 62}{space 2} .0242131{col 73}{space 1}    2.74{col 82}{space 3}0.007{col 90}{space 4} .0183603{col 103}{space 3} .1144485
{txt}{space 48} {c |}
{space 36}1.supervisor {c |}{col 50}{res}{space 2} .1426734{col 62}{space 2} .0156733{col 73}{space 1}    9.10{col 82}{space 3}0.000{col 90}{space 4} .1115741{col 103}{space 3} .1737727
{txt}{space 48} {c |}
{space 13}supervisor#c.ln_ratio_mnmsup_mnmsub {c |}
{space 46}1  {c |}{col 50}{res}{space 2}  .042059{col 62}{space 2} .0301975{col 73}{space 1}    1.39{col 82}{space 3}0.167{col 90}{space 4}-.0178593{col 103}{space 3} .1019774
{txt}{space 48} {c |}
{space 25}minority_het#supervisor {c |}
{space 44}1 1  {c |}{col 50}{res}{space 2}  .014635{col 62}{space 2} .0097398{col 73}{space 1}    1.50{col 82}{space 3}0.136{col 90}{space 4} -.004691{col 103}{space 3} .0339609
{txt}{space 44}2 1  {c |}{col 50}{res}{space 2} .0073175{col 62}{space 2}  .017031{col 73}{space 1}    0.43{col 82}{space 3}0.668{col 90}{space 4}-.0264757{col 103}{space 3} .0411108
{txt}{space 48} {c |}
minority_het#supervisor#c.ln_ratio_mnmsup_mnmsub {c |}
{space 44}1 1  {c |}{col 50}{res}{space 2} .0549444{col 62}{space 2} .0220357{col 73}{space 1}    2.49{col 82}{space 3}0.014{col 90}{space 4} .0112208{col 103}{space 3}  .098668
{txt}{space 44}2 1  {c |}{col 50}{res}{space 2}-.0047446{col 62}{space 2} .0334009{col 73}{space 1}   -0.14{col 82}{space 3}0.887{col 90}{space 4}-.0710192{col 103}{space 3} .0615301
{txt}{space 48} {c |}
{space 23}ln_ratio_min_tot_nmin_tot {c |}{col 50}{res}{space 2} .0583194{col 62}{space 2} .0483338{col 73}{space 1}    1.21{col 82}{space 3}0.230{col 90}{space 4}-.0375853{col 103}{space 3}  .154224
{txt}{space 42}gender {c |}{col 50}{res}{space 2}-.0267711{col 62}{space 2} .0046375{col 73}{space 1}   -5.77{col 82}{space 3}0.000{col 90}{space 4}-.0359729{col 103}{space 3}-.0175693
{txt}{space 32}topoffminority_2 {c |}{col 50}{res}{space 2} .0101954{col 62}{space 2} .0055142{col 73}{space 1}    1.85{col 82}{space 3}0.067{col 90}{space 4}-.0007459{col 103}{space 3} .0211368
{txt}{space 28}lntotworkforce_count {c |}{col 50}{res}{space 2} .0650719{col 62}{space 2} .0446023{col 73}{space 1}    1.46{col 82}{space 3}0.148{col 90}{space 4}-.0234288{col 103}{space 3} .1535726
{txt}{space 20}ln_professionals_total_ratio {c |}{col 50}{res}{space 2} .0194747{col 62}{space 2} .0522817{col 73}{space 1}    0.37{col 82}{space 3}0.710{col 90}{space 4}-.0842636{col 103}{space 3} .1232129
{txt}{space 48} {c |}
{space 40}agencyid {c |}
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{space 43}2011  {c |}{col 50}{res}{space 2}-.0018635{col 62}{space 2} .0039048{col 73}{space 1}   -0.48{col 82}{space 3}0.634{col 90}{space 4}-.0096114{col 103}{space 3} .0058845
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{txt}{space 43}2013  {c |}{col 50}{res}{space 2} .0024906{col 62}{space 2} .0059506{col 73}{space 1}    0.42{col 82}{space 3}0.676{col 90}{space 4}-.0093168{col 103}{space 3} .0142979
{txt}{space 43}2014  {c |}{col 50}{res}{space 2}-.0025903{col 62}{space 2}  .006939{col 73}{space 1}   -0.37{col 82}{space 3}0.710{col 90}{space 4}-.0163587{col 103}{space 3} .0111782
{txt}{space 43}2015  {c |}{col 50}{res}{space 2}-.0101095{col 62}{space 2} .0084526{col 73}{space 1}   -1.20{col 82}{space 3}0.235{col 90}{space 4}-.0268813{col 103}{space 3} .0066622
{txt}{space 43}2016  {c |}{col 50}{res}{space 2} -.013351{col 62}{space 2} .0085006{col 73}{space 1}   -1.57{col 82}{space 3}0.119{col 90}{space 4}-.0302182{col 103}{space 3} .0035161
{txt}{space 43}2017  {c |}{col 50}{res}{space 2}-.0028764{col 62}{space 2} .0124718{col 73}{space 1}   -0.23{col 82}{space 3}0.818{col 90}{space 4}-.0276232{col 103}{space 3} .0218704
{txt}{space 43}2018  {c |}{col 50}{res}{space 2}-.0293412{col 62}{space 2} .0114045{col 73}{space 1}   -2.57{col 82}{space 3}0.012{col 90}{space 4}-.0519702{col 103}{space 3}-.0067122
{txt}{space 43}2019  {c |}{col 50}{res}{space 2}-.0703072{col 62}{space 2} .0127527{col 73}{space 1}   -5.51{col 82}{space 3}0.000{col 90}{space 4}-.0956114{col 103}{space 3}-.0450031
{txt}{space 48} {c |}
{space 43}_cons {c |}{col 50}{res}{space 2} .0331254{col 62}{space 2} .5635609{col 73}{space 1}    0.06{col 82}{space 3}0.953{col 90}{space 4}-1.085102{col 103}{space 3} 1.151353
{txt}{hline 49}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16} 2,412,654{col 28} -1879502{col 39} -1829720{col 50}    25{col 58}  3659490{col 69}  3659807
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] IC note}}.{p_end}

{com}. *
. 
. ** BY NON-SUPERVISORS RESPONDENT: BETWEEN-IDENTITY "IN" GROUP VERSUS "OUT" GROUP STATUS DIFFERENTIAL BETWEEN MINORITY/NON-MINORITY RESPONDENTS **
. 
. lincom c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0375523{col 26}{space 2} .0361836{col 37}{space 1}    1.04{col 46}{space 3}0.302{col 54}{space 4}-.0342438{col 67}{space 3} .1093485
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  1.minority_het#c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.minority_het#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}  .024794{col 26}{space 2}   .01839{col 37}{space 1}    1.35{col 46}{space 3}0.181{col 54}{space 4}-.0116958{col 67}{space 3} .0612837
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  2.minority_het#c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}2.minority_het#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0664044{col 26}{space 2} .0242131{col 37}{space 1}    2.74{col 46}{space 3}0.007{col 54}{space 4} .0183603{col 67}{space 3} .1144485
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. lincom  2.minority_het#c.ln_ratio_mnmsup_mnmsub - 1.minority_het#c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1}{space 1}{res}- 1.minority_het#c.ln_ratio_mnmsup_mnmsub + 2.minority_het#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0416104{col 26}{space 2} .0196223{col 37}{space 1}    2.12{col 46}{space 3}0.036{col 54}{space 4} .0026756{col 67}{space 3} .0805453
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *
. *
. *
. *
. *
. 
. 
. ** BY SUPERVISOR RESPONDENT: BETWEEN-IDENTITY "IN" GROUP VERSUS "OUT" GROUP STATUS DIFFERENTIAL BETWEEN MINORITY/NON-MINORITY RESPONDENTS **
. 
. lincom c.ln_ratio_mnmsup_mnmsub+ 1.supervisor#c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}ln_ratio_mnmsup_mnmsub + 1.supervisor#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0796114{col 26}{space 2} .0448962{col 37}{space 1}    1.77{col 46}{space 3}0.079{col 54}{space 4}-.0094725{col 67}{space 3} .1686952
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  1.minority_het#c.ln_ratio_mnmsup_mnmsub +  1.minority_het#1.supervisor#c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.minority_het#c.ln_ratio_mnmsup_mnmsub + 1.minority_het#1.supervisor#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0797384{col 26}{space 2} .0250098{col 37}{space 1}    3.19{col 46}{space 3}0.002{col 54}{space 4} .0301135{col 67}{space 3} .1293633
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. lincom  2.minority_het#c.ln_ratio_mnmsup_mnmsub +  2.minority_het#1.supervisor#c.ln_ratio_mnmsup_mnmsub

{p 0 7}{space 1}{text:( 1)}{space 1} {res}2.minority_het#c.ln_ratio_mnmsup_mnmsub + 2.minority_het#1.supervisor#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0616599{col 26}{space 2} .0267606{col 37}{space 1}    2.30{col 46}{space 3}0.023{col 54}{space 4} .0085611{col 67}{space 3} .1147587
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. lincom  2.minority_het#c.ln_ratio_mnmsup_mnmsub +  2.minority_het#1.supervisor#c.ln_ratio_mnmsup_mnmsub - (1.minority_het#c.ln_ratio_mnmsup_mnmsub +  1.minority_het#1.supervisor#c.ln_ratio_mnmsup_mnmsub)

{p 0 7}{space 1}{text:( 1)}{space 1}{space 1}{res}- 1.minority_het#c.ln_ratio_mnmsup_mnmsub + 2.minority_het#c.ln_ratio_mnmsup_mnmsub - 1.minority_het#1.supervisor#c.ln_ratio_mnmsup_mnmsub + 2.minority_het#1.supervisor#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.0180785{col 26}{space 2} .0325254{col 37}{space 1}   -0.56{col 46}{space 3}0.580{col 54}{space 4}-.0826159{col 67}{space 3} .0464589
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. *
. 
. 
. * SUPERVISOR - NON-SUPERVISORY DIFFERENCE AMONG MINORITY RESPONDENT DIFFERENCES [MINORITY MEN RESPONDENTS FOLLOWED BY MINORITY WOMEN RESPONDENTS] -- DO NOT PLOT IN GRAPHS [ONLY FOR TEXT]!
. 
. lincom  1.minority_het#c.ln_ratio_mnmsup_mnmsub +  1.minority_het#1.supervisor#c.ln_ratio_mnmsup_mnmsub - (1.minority_het#c.ln_ratio_mnmsup_mnmsub)

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.minority_het#1.supervisor#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0549444{col 26}{space 2} .0220357{col 37}{space 1}    2.49{col 46}{space 3}0.014{col 54}{space 4} .0112208{col 67}{space 3}  .098668
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. lincom  2.minority_het#c.ln_ratio_mnmsup_mnmsub +  2.minority_het#1.supervisor#c.ln_ratio_mnmsup_mnmsub - (2.minority_het#c.ln_ratio_mnmsup_mnmsub)

{p 0 7}{space 1}{text:( 1)}{space 1} {res}2.minority_het#1.supervisor#c.ln_ratio_mnmsup_mnmsub = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}lndiversit~j{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.0047446{col 26}{space 2} .0334009{col 37}{space 1}   -0.14{col 46}{space 3}0.887{col 54}{space 4}-.0710192{col 67}{space 3} .0615301
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. clear
{txt}
{com}. 
. *** FIGURE G1.1: PLOT ELASTICITY MARGINAL EFFECTS FOR MODELS G1.1 & G2.1 USING LINCOMS ABOVE FOR EACH MODEL: PATTERN AFTER COMPARABLE SET OF MANUSCRIPT GRAPHICS/FIGURES [FIGURE 1]
. import excel "C:\Users\jungy\Dropbox\Discrimination Project_for me\Organizational Diversity\Graph\New Graphs_ROPPA 2023 Version\Appendix G\figureg1.xlsx", sheet("Sheet1") firstrow
{res}{text}(5 vars, 8 obs)

{com}. destring, replace
{txt}row already numeric; no {res}replace
{txt}group already numeric; no {res}replace
{txt}estimates already numeric; no {res}replace
{txt}low95 already numeric; no {res}replace
{txt}high95 already numeric; no {res}replace
{txt}
{com}.  
. set scheme sj, permanently 
{txt}({cmd:set scheme} preference recorded)

{com}. graph set window fontface "Century Schoolbook"
{txt}
{com}. 
. twoway (rcap low95 high95 row, vert) (scatter estimates row if group ==1, msymbol(square) mcolor(orange))(scatter estimates row if group ==2, msymbol(circle_hollow) mcolor(orange))(scatter estimates row if group ==3, msymbol(square) mcolor(navy))(scatter estimates row if group ==4, msymbol(circle_hollow) mcolor(navy)), legend(row(1) order(2 "Gender" 4 "Race/Ethnicity") pos(6)) title("FIGURE G1" "Relationship Between Authority Differentials and D&I Employee Evaluations" "(By Respondent Single Social Identity Group)" "[Omitting Supervisory Descriptive Representation Measure as a Covariate]", size(small)) ylabel(-0.1(.1)0.3, labsize (small) angle(horizon)) xtitle("AD Estimates [Differentials by Responsdent Single Social Identity Group]" "(Models 1&2)", size(small)) xlabel("", noticks) yline(0, lpattern(dash) lcolor(gs8)) aspect(.5)
{res}{txt}
{com}. 
. clear
{txt}
{com}. 
. *** FIGURE G2.1: PLOT ELASTICITY MARGINAL EFFECTS FOR MODELS G3.1 & G4.1 USING LINCOMS ABOVE FOR EACH MODEL: PATTERN AFTER COMPARABLE SET OF MANUSCRIPT GRAPHICS/FIGURES [FIGURE 2]
. import excel "C:\Users\jungy\Dropbox\Discrimination Project_for me\Organizational Diversity\Graph\New Graphs_ROPPA 2023 Version\Appendix G\figureg2.xlsx", sheet("Sheet1") firstrow
{res}{text}(10 vars, 12 obs)

{com}. destring, replace
{txt}row already numeric; no {res}replace
{txt}group already numeric; no {res}replace
{txt}estimates already numeric; no {res}replace
{txt}low95 already numeric; no {res}replace
{txt}high95 already numeric; no {res}replace
{txt}F already numeric; no {res}replace
{txt}G already numeric; no {res}replace
{txt}H already numeric; no {res}replace
{txt}I already numeric; no {res}replace
{txt}J already numeric; no {res}replace
{txt}
{com}.  
. set scheme sj, permanently 
{txt}({cmd:set scheme} preference recorded)

{com}. graph set window fontface "Century Schoolbook"
{txt}
{com}. 
. twoway (rcap low95 high95 row, vert) (scatter estimates row if group ==1, msymbol(square) mcolor(orange))(scatter estimates row if group ==2, msymbol(circle_hollow) mcolor(orange)) (scatter estimates row if group ==3, msymbol(diamond_hollow) mcolor(orange))(scatter estimates row if group ==4, msymbol(triangle_hollow) mcolor(orange))(scatter estimates row if group ==5, msymbol(square) mcolor(navy))(scatter estimates row if group ==6, msymbol(circle_hollow) mcolor(navy))(scatter estimates row if group ==7, msymbol(diamond_hollow) mcolor(navy))(scatter estimates row if group ==8, msymbol(triangle_hollow) mcolor(navy)), legend(row(1) order(2 "Gender" 6 "Race/Ethnicity") pos(6)) title("FIGURE G2"  "Relationship Between Authority Differentials and D&I Employee Evaluations" `"(By Respondent Intersectional Social Identity Group)"' "[Omitting Supervisory Descriptive Representation Measure as a Covariate]", size(small)) ylabel(-0.1(.1)0.3, labsize (small) angle(horizon)) xtitle("Gender AD Effects: by Respondent Intersectionality Group   Race/Ethnicity AD Effects: by Respondent Intersectionality Group", size(vsmall)) xlabel("", noticks) yline(0, lpattern(dash) lcolor(gs8)) aspect(.5)
{res}{txt}
{com}. 
. clear
{txt}
{com}. 
. *** FIGURE G3.1: PLOT ELASTICITY MARGINAL EFFECTS FOR MODELS G5.1-G8.1 USING LINCOMS ABOVE FOR EACH MODEL: PATTERN AFTER COMPARABLE SET OF MANUSCRIPT GRAPHICS/FIGURES [FIGURE 3]
. import excel "C:\Users\jungy\Dropbox\Discrimination Project_for me\Organizational Diversity\Graph\New Graphs_ROPPA 2023 Version\Appendix G\figureg3.xlsx", sheet("Sheet1") firstrow
{res}{text}(14 vars, 18 obs)

{com}. destring, replace
{txt}row already numeric; no {res}replace
{txt}group already numeric; no {res}replace
{txt}estimates already numeric; no {res}replace
{txt}low95 already numeric; no {res}replace
{txt}high95 already numeric; no {res}replace
{txt}F already numeric; no {res}replace
{txt}G already numeric; no {res}replace
{txt}H already numeric; no {res}replace
{txt}I already numeric; no {res}replace
{txt}J already numeric; no {res}replace
{txt}K already numeric; no {res}replace
{txt}L already numeric; no {res}replace
{txt}M already numeric; no {res}replace
{txt}N already numeric; no {res}replace
{txt}
{com}.  
. set scheme sj, permanently 
{txt}({cmd:set scheme} preference recorded)

{com}. graph set window fontface "Century Schoolbook"
{txt}
{com}. 
. twoway (rcap low95 high95 row, vert) (scatter estimates row if group ==1, msymbol(square) mcolor(orange))(scatter estimates row if group ==2, msymbol(square_hollow) mcolor(orange)) (scatter estimates row if group ==3, msymbol(square) mcolor(navy))(scatter estimates row if group ==4, msymbol(square_hollow) mcolor(navy))(scatter estimates row if group ==5, msymbol(square) mcolor(orange))(scatter estimates row if group ==6, msymbol(circle_hollow) mcolor(orange))(scatter estimates row if group ==7, msymbol(diamond_hollow) mcolor(orange))(scatter estimates row if group ==8, msymbol(triangle_hollow) mcolor(orange))(scatter estimates row if group ==9, msymbol(square) mcolor(navy))(scatter estimates row if group ==10, msymbol(circle_hollow) mcolor(navy))(scatter estimates row if group ==11, msymbol(diamond_hollow) mcolor(navy))(scatter estimates row if group ==12, msymbol(triangle_hollow) mcolor(navy)), legend(row(1) order(2 "Gender" 4 "Race/Ethnicity") pos(6)) title("FIGURE G3" "Relationship Between Authority Differentials and D&I Employee Evaluations" "(Non-Supervisory Respondents: Single and Intersectional Social Identity Groups)" "[Omitting Supervisory Descriptive Representation Measure as a Covariate]", size(small)) ylabel(-0.1(.1)0.3, labsize (small) angle(horizon)) xtitle("AD Effects: by Respondent Single Identity Group     AD Effects: by Respondent Intersectionality Group", size(vsmall)) xlabel("", noticks) yline(0, lpattern(dash) lcolor(gs8)) aspect(.5)
{res}{txt}
{com}. 
. clear
{txt}
{com}. 
. 
. 
. *** FIGURE G4.1: PLOT ELASTICITY MARGINAL EFFECTS FOR MODELS G5.1-G8.1 USING LINCOMS ABOVE FOR EACH MODEL: PATTERN AFTER COMPARABLE SET OF MANUSCRIPT GRAPHICS/FIGURES [FIGURE 4]
. import excel "C:\Users\jungy\Dropbox\Discrimination Project_for me\Organizational Diversity\Graph\New Graphs_ROPPA 2023 Version\Appendix G\figureg4.xlsx", sheet("Sheet1") firstrow
{res}{text}(14 vars, 18 obs)

{com}. destring, replace
{txt}row already numeric; no {res}replace
{txt}group already numeric; no {res}replace
{txt}estimates already numeric; no {res}replace
{txt}low95 already numeric; no {res}replace
{txt}high95 already numeric; no {res}replace
{txt}F already numeric; no {res}replace
{txt}G already numeric; no {res}replace
{txt}H already numeric; no {res}replace
{txt}I already numeric; no {res}replace
{txt}J already numeric; no {res}replace
{txt}K already numeric; no {res}replace
{txt}L already numeric; no {res}replace
{txt}M already numeric; no {res}replace
{txt}N already numeric; no {res}replace
{txt}
{com}. 
. set scheme sj, permanently 
{txt}({cmd:set scheme} preference recorded)

{com}. graph set window fontface "Century Schoolbook"
{txt}
{com}. 
. twoway (rcap low95 high95 row, vert) (scatter estimates row if group ==1, msymbol(square) mcolor(orange))(scatter estimates row if group ==2, msymbol(square_hollow) mcolor(orange)) (scatter estimates row if group ==3, msymbol(square) mcolor(navy))(scatter estimates row if group ==4, msymbol(square_hollow) mcolor(navy))(scatter estimates row if group ==5, msymbol(square) mcolor(orange))(scatter estimates row if group ==6, msymbol(circle_hollow) mcolor(orange))(scatter estimates row if group ==7, msymbol(diamond_hollow) mcolor(orange))(scatter estimates row if group ==8, msymbol(triangle_hollow) mcolor(orange))(scatter estimates row if group ==9, msymbol(square) mcolor(navy))(scatter estimates row if group ==10, msymbol(circle_hollow) mcolor(navy))(scatter estimates row if group ==11, msymbol(diamond_hollow) mcolor(navy))(scatter estimates row if group ==12, msymbol(triangle_hollow) mcolor(navy)), legend(row(1) order(2 "Gender" 4 "Race/Ethnicity") pos(6)) title("FIGURE G4" "Relationship Between Authority Differentials and D&I Employee Evaluations" "(Supervisor Respondents: Single and Intersectional Social Identity Groups)" "[Omitting Supervisory Descriptive Representation Measure as a Covariate]", size(small)) ylabel(-0.1(.1)0.3, labsize (small) angle(horizon)) xtitle("AD Effects: by Respondent Single Identity Group     AD Effects: by Respondent Intersectionality Group", size(vsmall)) xlabel("", noticks) yline(0, lpattern(dash) lcolor(gs8)) aspect(.5)
{res}{txt}
{com}. 
. clear
{txt}
{com}. 
. 
. 
. *** FIGURE G1.2: PLOT ELASTICITY MARGINAL EFFECTS FOR MODELS G1.2-G2.2 USING LINCOMS ABOVE FOR EACH MODEL: PATTERN AFTER COMPARABLE SET OF MANUSCRIPT GRAPHICS/FIGURES [FIGURE 5]
. import excel "C:\Users\jungy\Dropbox\Discrimination Project_for me\Organizational Diversity\Graph\New Graphs_ROPPA 2023 Version\Appendix G\figureg5.xlsx", sheet("Sheet1") firstrow
{res}{text}(5 vars, 8 obs)

{com}. destring, replace
{txt}row already numeric; no {res}replace
{txt}group already numeric; no {res}replace
{txt}estimates already numeric; no {res}replace
{txt}low95 already numeric; no {res}replace
{txt}high95 already numeric; no {res}replace
{txt}
{com}. 
. set scheme sj, permanently 
{txt}({cmd:set scheme} preference recorded)

{com}. graph set window fontface "Century Schoolbook"
{txt}
{com}. 
. twoway (rcap low95 high95 row, vert) (scatter estimates row if group ==1, msymbol(square) mcolor(orange))(scatter estimates row if group ==2, msymbol(circle_hollow) mcolor(orange))(scatter estimates row if group ==3, msymbol(square) mcolor(navy))(scatter estimates row if group ==4, msymbol(circle_hollow) mcolor(navy)), legend(row(1) order(2 "Gender" 4 "Race/Ethnicity") pos(6)) title("FIGURE G5" "Relationship Between Authority Differentials and D&I Employee Evaluations" "(By Respondent Single Social Identity Group)" "[Omitting 'Extreme' above Parity Values of AD Measure]", size(small)) ylabel(-0.1(.1)0.3, labsize (small) angle(horizon)) xtitle("AD Estimates [Differentials by Responsdent Single Social Identity Group]" "(Models 1&2)", size(small)) xlabel("", noticks) yline(0, lpattern(dash) lcolor(gs8)) aspect(.5)
{res}{txt}
{com}. 
. clear
{txt}
{com}. 
. 
. 
. *** FIGURE G2.2: PLOT ELASTICITY MARGINAL EFFECTS FOR MODELS G3.2-G4.2 USING LINCOMS ABOVE FOR EACH MODEL: PATTERN AFTER COMPARABLE SET OF MANUSCRIPT GRAPHICS/FIGURES [FIGURE 6]
. import excel "C:\Users\jungy\Dropbox\Discrimination Project_for me\Organizational Diversity\Graph\New Graphs_ROPPA 2023 Version\Appendix G\figureg6.xlsx", sheet("Sheet1") firstrow
{res}{text}(10 vars, 12 obs)

{com}. destring, replace
{txt}row already numeric; no {res}replace
{txt}group already numeric; no {res}replace
{txt}estimates already numeric; no {res}replace
{txt}low95 already numeric; no {res}replace
{txt}high95 already numeric; no {res}replace
{txt}F already numeric; no {res}replace
{txt}G already numeric; no {res}replace
{txt}H already numeric; no {res}replace
{txt}I already numeric; no {res}replace
{txt}J already numeric; no {res}replace
{txt}
{com}.  
. set scheme sj, permanently 
{txt}({cmd:set scheme} preference recorded)

{com}. graph set window fontface "Century Schoolbook"
{txt}
{com}. 
. twoway (rcap low95 high95 row, vert) (scatter estimates row if group ==1, msymbol(square) mcolor(orange))(scatter estimates row if group ==2, msymbol(circle_hollow) mcolor(orange)) (scatter estimates row if group ==3, msymbol(diamond_hollow) mcolor(orange))(scatter estimates row if group ==4, msymbol(triangle_hollow) mcolor(orange))(scatter estimates row if group ==5, msymbol(square) mcolor(navy))(scatter estimates row if group ==6, msymbol(circle_hollow) mcolor(navy))(scatter estimates row if group ==7, msymbol(diamond_hollow) mcolor(navy))(scatter estimates row if group ==8, msymbol(triangle_hollow) mcolor(navy)), legend(row(1) order(2 "Gender" 6 "Race/Ethnicity") pos(6)) title("FIGURE G6"  "Relationship Between Authority Differentials and D&I Employee Evaluations" `"(By Respondent Intersectional Social Identity Group)"' "[Omitting 'Extreme' above Parity Values of AD Measure]", size(small)) ylabel(-0.1(.1)0.3, labsize (small) angle(horizon)) xtitle("Gender AD Effects: by Respondent Intersectionality Group   Race/Ethnicity AD Effects: by Respondent Intersectionality Group", size(vsmall)) xlabel("", noticks) yline(0, lpattern(dash) lcolor(gs8)) aspect(.5)
{res}{txt}
{com}. 
. clear
{txt}
{com}. 
. 
. 
. *** FIGURE G3.2: PLOT ELASTICITY MARGINAL EFFECTS FOR MODELS G5.2-G8.2 USING LINCOMS ABOVE FOR EACH MODEL: PATTERN AFTER COMPARABLE SET OF MANUSCRIPT GRAPHICS/FIGURES [FIGURE 7]
. import excel "C:\Users\jungy\Dropbox\Discrimination Project_for me\Organizational Diversity\Graph\New Graphs_ROPPA 2023 Version\Appendix G\figureg7.xlsx", sheet("Sheet1") firstrow
{res}{text}(14 vars, 18 obs)

{com}. destring, replace
{txt}row already numeric; no {res}replace
{txt}group already numeric; no {res}replace
{txt}estimates already numeric; no {res}replace
{txt}low95 already numeric; no {res}replace
{txt}high95 already numeric; no {res}replace
{txt}F already numeric; no {res}replace
{txt}G already numeric; no {res}replace
{txt}H already numeric; no {res}replace
{txt}I already numeric; no {res}replace
{txt}J already numeric; no {res}replace
{txt}K already numeric; no {res}replace
{txt}L already numeric; no {res}replace
{txt}M already numeric; no {res}replace
{txt}N already numeric; no {res}replace
{txt}
{com}.  
. set scheme sj, permanently 
{txt}({cmd:set scheme} preference recorded)

{com}. graph set window fontface "Century Schoolbook"
{txt}
{com}. 
. twoway (rcap low95 high95 row, vert) (scatter estimates row if group ==1, msymbol(square) mcolor(orange))(scatter estimates row if group ==2, msymbol(square_hollow) mcolor(orange)) (scatter estimates row if group ==3, msymbol(square) mcolor(navy))(scatter estimates row if group ==4, msymbol(square_hollow) mcolor(navy))(scatter estimates row if group ==5, msymbol(square) mcolor(orange))(scatter estimates row if group ==6, msymbol(circle_hollow) mcolor(orange))(scatter estimates row if group ==7, msymbol(diamond_hollow) mcolor(orange))(scatter estimates row if group ==8, msymbol(triangle_hollow) mcolor(orange))(scatter estimates row if group ==9, msymbol(square) mcolor(navy))(scatter estimates row if group ==10, msymbol(circle_hollow) mcolor(navy))(scatter estimates row if group ==11, msymbol(diamond_hollow) mcolor(navy))(scatter estimates row if group ==12, msymbol(triangle_hollow) mcolor(navy)), legend(row(1) order(2 "Gender" 4 "Race/Ethnicity") pos(6)) title("FIGURE G7" "Relationship Between Authority Differentials and D&I Employee Evaluations" "(Non-Supervisory Respondents: Single and Intersectional Social Identity Groups)" "[Omitting 'Extreme' above Parity Values of AD Measure]", size(small)) ylabel(-0.1(.1)0.2, labsize (small) angle(horizon)) xtitle("AD Effects: by Respondent Single Identity Group     AD Effects: by Respondent Intersectionality Group", size(vsmall)) xlabel("", noticks) yline(0, lpattern(dash) lcolor(gs8)) aspect(.5)
{res}{txt}
{com}. 
. clear
{txt}
{com}. 
. 
. 
. *** FIGURE G4.2: PLOT ELASTICITY MARGINAL EFFECTS FOR MODELS G5.2-G8.2 USING LINCOMS ABOVE FOR EACH MODEL: PATTERN AFTER COMPARABLE SET OF MANUSCRIPT GRAPHICS/FIGURES [FIGURE 8]
. import excel "C:\Users\jungy\Dropbox\Discrimination Project_for me\Organizational Diversity\Graph\New Graphs_ROPPA 2023 Version\Appendix G\figureg8.xlsx", sheet("Sheet1") firstrow
{res}{text}(14 vars, 18 obs)

{com}. destring, replace
{txt}row already numeric; no {res}replace
{txt}group already numeric; no {res}replace
{txt}estimates already numeric; no {res}replace
{txt}low95 already numeric; no {res}replace
{txt}high95 already numeric; no {res}replace
{txt}F already numeric; no {res}replace
{txt}G already numeric; no {res}replace
{txt}H already numeric; no {res}replace
{txt}I already numeric; no {res}replace
{txt}J already numeric; no {res}replace
{txt}K already numeric; no {res}replace
{txt}L already numeric; no {res}replace
{txt}M already numeric; no {res}replace
{txt}N already numeric; no {res}replace
{txt}
{com}. 
. set scheme sj, permanently 
{txt}({cmd:set scheme} preference recorded)

{com}. graph set window fontface "Century Schoolbook"
{txt}
{com}. 
. twoway (rcap low95 high95 row, vert) (scatter estimates row if group ==1, msymbol(square) mcolor(orange))(scatter estimates row if group ==2, msymbol(square_hollow) mcolor(orange)) (scatter estimates row if group ==3, msymbol(square) mcolor(navy))(scatter estimates row if group ==4, msymbol(square_hollow) mcolor(navy))(scatter estimates row if group ==5, msymbol(square) mcolor(orange))(scatter estimates row if group ==6, msymbol(circle_hollow) mcolor(orange))(scatter estimates row if group ==7, msymbol(diamond_hollow) mcolor(orange))(scatter estimates row if group ==8, msymbol(triangle_hollow) mcolor(orange))(scatter estimates row if group ==9, msymbol(square) mcolor(navy))(scatter estimates row if group ==10, msymbol(circle_hollow) mcolor(navy))(scatter estimates row if group ==11, msymbol(diamond_hollow) mcolor(navy))(scatter estimates row if group ==12, msymbol(triangle_hollow) mcolor(navy)), legend(row(1) order(2 "Gender" 4 "Race/Ethnicity") pos(6)) title("FIGURE G8" "Relationship Between Authority Differentials and D&I Employee Evaluations" "(Supervisor Respondents: Single and Intersectional Social Identity Groups)" "[Omitting 'Extreme' above Parity Values of AD Measure]", size(small)) ylabel(-0.1(.1)0.3, labsize (small) angle(horizon)) xtitle("AD Effects: by Respondent Single Identity Group     AD Effects: by Respondent Intersectionality Group", size(vsmall)) xlabel("", noticks) yline(0, lpattern(dash) lcolor(gs8)) aspect(.5)
{res}{txt}
{com}.   
. 
. 
. 
.  
.    
.    
. 
. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\jungy\Dropbox\DISCRIMINATION PROJECT\Organizational Diversity\Statistics\Krause & Park.Authority Differentials.APPENDIX G RESULTS.08-07-2024.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 7 Aug 2024, 21:51:28
{txt}{.-}
{smcl}
{txt}{sf}{ul off}